Ali Taimori - Academia.edu (original) (raw)

Papers by Ali Taimori

Research paper thumbnail of A Novel Fit-Flexible Fluorescence Soft Imager: Tri-Sensing of Intensity, Fall-Time, and Life Profile

IEEE transactions on bio-medical engineering/IEEE transactions on biomedical engineering, 2024

Research paper thumbnail of Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: a Review

Methods and Applications in Fluorescence, Dec 5, 2023

Research paper thumbnail of Forensic discrimination between traditional and compressive imaging by blurring kernel investigation

Multimedia Tools and Applications, Oct 11, 2023

Research paper thumbnail of A novel fit-flexible fluorescence imager: Tri-sensing of intensity, fall-time, and life profile

The preprint presents a novel sensing approach to time-resolved fluorescence imaging. It is also ... more The preprint presents a novel sensing approach to time-resolved fluorescence imaging. It is also supported by supplementary materials.

Research paper thumbnail of Fast and Robust Single-Exponential Decay Recovery From Noisy Fluorescence Lifetime Imaging

IEEE Transactions on Biomedical Engineering

Research paper thumbnail of A part-level learning strategy for JPEG image recompression detection

Multimedia Tools and Applications, 2021

Research paper thumbnail of A novel forensic image analysis tool for discovering double JPEG compression clues

Multimedia Tools and Applications, 2016

This paper presents a novel technique to discover double JPEG compression traces. Existing detect... more This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool discriminates single compressed images from double counterparts, estimates the first quantization in double compression, and localizes tampered regions in a forgery examination. Extensive experiments on three databases demonstrate results are robust among different quality levels.

Research paper thumbnail of Quantization-Unaware Double JPEG Compression Detection

Journal of Mathematical Imaging and Vision, 2015

The current double JPEG compression detection techniques identify whether or not an JPEG image fi... more The current double JPEG compression detection techniques identify whether or not an JPEG image file has undergone the compression twice, by knowing its embedded quantization table. This paper addresses another forensic scenario in which the quantization table of a JPEG file is not explicitly or reliably known, which may compel the forensic analyst to blindly reveal the recompression clues. To do this, we first statistically analyze the theory behind quantized Alternating Current (AC) modes in JPEG compression and show that the number of quantized AC modes required to detect double compression is a function of both the image's block texture and the compression's quality level in a fresh formulation. Consequently, a new double compression detection algorithm is proposed that exploits footprints introduced by all non-zero and zero AC modes based on Benford's law in a low-dimensional representation via PCA. Then, some evaluation frameworks are constructed to assess the robustness and generalization of the proposed method on

Research paper thumbnail of A new deformable mesh model for face tracking using edge based features and novel sets of energy functions

Multimedia Tools and Applications, 2014

ABSTRACT This paper presents a new method for automatic human face locating and tracking. The pro... more ABSTRACT This paper presents a new method for automatic human face locating and tracking. The proposed method consists of two modules including face locating and face tracking. The face locating module has a hierarchical structure, which consists of a skin color classifier together with AdaBoost based face detectors. The face tracking module is considered to be the main contribution of the paper. The module is based on the unstructured 2-D triangular deformable meshes, which employs a new robust and illumination insensitive feature extraction and matching algorithms as well as new sets of mesh energy functions. The feature extraction and matching algorithms are established upon edge points and their representation using fuzzy set theory, which is called fuzzy edges. For matching features, a multiresolution algorithm is utilized based on fuzzy edges and edge pyramid. The new mesh energy functions are also employed to manage both rigid and non-rigid motions in the head and face. Experimental results demonstrate the accuracy and stability of the proposed method for both face locating and face tracking.

Research paper thumbnail of A proper transform for satisfying Benford's Law and its application to double JPEG image forensics

2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2012

This paper presents a new transform domain to evaluate the goodness of fit of natural image data ... more This paper presents a new transform domain to evaluate the goodness of fit of natural image data to the common Benford's Law. The evaluation is made by three statistical fitness criteria includ ing Pearson's chi-square test statistic, normalized cross correlation and a distance measure based on symmetrized Kullback-Leibler di vergence. It is shown that the serial combination of variance filtering and block 2-D discrete cosine transform reveals the best goodness of fit for the first significant digit. We also show that the proposed transform domain brings reasonable fit for the second, third and fourth significant digits. As an application, the proposed transform domain is utilized to detect image manipulation by distinguishing single compressed images from doubly compressed ones.

Research paper thumbnail of Vision based flying vehicle tracking

2008 IEEE International Conference on Systems, Man and Cybernetics, 2008

In this paper, we have proposed an efficient vision based method for the tracking of flying vehic... more In this paper, we have proposed an efficient vision based method for the tracking of flying vehicles via a moving video camera. The suggested approach is based on the combination of the feature extraction and matching algorithm as well as the 2D deformable mesh surfaces. We have developed a new set of energy functions for mesh to improve the performance

Research paper thumbnail of Automatic Human Face Detection and Tracking using Cascaded Classifiers and Deformable Meshes

sid.ir

In this paper, we propose a novel method for fully automatic detection and tracking of human head... more In this paper, we propose a novel method for fully automatic detection and tracking of human heads and faces in video sequences. The proposed algorithm consists of two modules: a face detection module and a face tracking module. The Detection module, detects the face region and approximates it with an ellipse at the first frame using a modified version of AdaBoost cascaded classifier. The detection module is capable of considering the 2-D head pose rotation. The tracking module utiliyes a combination of deformable mesh energy minimization and feature matching approaches. In order to track a face, features are extracted in the face region to tessellate the human face with triangular unstructured meshes. For tracking a mesh, it is necessary to define mesh energies including internal and external energies. We have used new energy definitions for both the internal and the external energies which can accurately track rigid and non-rigid motions of a face and facial features at subsequent frames. We tested the proposed method with different video samples like cluttered backgrounds, partial illumination variations, put on glasses, and 2-D and/or 3-D rotating and translating heads. The experimental results showed that the algorithm is rotation insensitive and has high accuracy, stability and also has convergence for face detection and tracking.

Research paper thumbnail of Measurement-Adaptive Sparse Image Sampling and Recovery

Cornell University - arXiv, Jun 9, 2017

This paper presents an adaptive and intelligent sparse model for digital image sampling and recov... more This paper presents an adaptive and intelligent sparse model for digital image sampling and recovery. In the proposed sampler, we adaptively determine the number of required samples for retrieving image based on space-frequency-gradient information content of image patches. By leveraging texture in space, sparsity locations in DCT domain, and directional decomposition of gradients, the sampler structure consists of a combination of uniform, random, and nonuniform sampling strategies. For reconstruction, we model the recovery problem as a two-state cellular automaton to iteratively restore image with scalable windows from generation to generation. We demonstrate the recovery algorithm quickly converges after a few generations for an image with arbitrary degree of texture. For a given number of measurements, extensive experiments on standard image-sets, infra-red, and mega-pixel range imaging devices show that the proposed measurement matrix considerably increases the overall recovery performance, or equivalently decreases the number of sampled pixels for a specific recovery quality compared to random sampling matrix and Gaussian linear combinations employed by the state-of-the-art compressive sensing methods. In practice, the proposed measurement-adaptive sampling/recovery framework includes various applications from intelligent compressive imaging-based acquisition devices to computer vision and graphics, and image processing technology. Simulation codes are available online for reproduction purposes.

Research paper thumbnail of An Image Dataset of Vehicles Front Views and Parts for Vehicle Detection, Localization and Alignment Applications

2020 10th International Symposium onTelecommunications (IST), 2020

This paper introduces a dataset of front view car images collected from traffic scenes. Each imag... more This paper introduces a dataset of front view car images collected from traffic scenes. Each image contains the bounding box of a vehicle which has cropped manually and accurately. The database also includes the parts corresponding to each frontal view car, i.e. left and right lights, left and right mirrors, grill, license plate, and bumper. In addition to vision-based applications such as object detection, verification, and part-based analysis, our database is specialized for testing and developing of trainable bounding box location prediction algorithms. We used a car localization algorithm for performance evaluation, which resulted in a localization accuracy of 87%. Also, we tested our dataset on Viola-Jones object detector and measured its performance in terms of false positive and false negative rates for images of whole-car as well as each individual part.

Research paper thumbnail of Forensic Discrimination between Traditional and Compressive Imaging Systems

ArXiv, 2018

Compressive sensing is a new technology for modern computational imaging systems. In comparison t... more Compressive sensing is a new technology for modern computational imaging systems. In comparison to widespread conventional image sensing, the compressive imaging paradigm requires specific forensic analysis techniques and tools. In this regards, one of basic scenarios in image forensics is to distinguish traditionally sensed images from sophisticated compressively sensed ones. To do this, we first mathematically and systematically model the imaging system based on compressive sensing technology. Afterwards, a simplified version of the whole model is presented, which is appropriate for forensic investigation applications. We estimate the nonlinear system of compressive sensing with a linear model. Then, we model the imaging pipeline as an inverse problem and demonstrate that different imagers have discriminative degradation kernels. Hence, blur kernels of various imaging systems have utilized as footprints for discriminating image acquisition sources. In order to accomplish the ident...

Research paper thumbnail of Adaptive Sparse Image Sampling and Recovery

IEEE Transactions on Computational Imaging, 2018

This paper presents an adaptive and intelligent sparse model for digital image sampling and recov... more This paper presents an adaptive and intelligent sparse model for digital image sampling and recovery. In the proposed sampler, we adaptively determine the number of required samples for retrieving image based on space-frequency-gradient information content of image patches. By leveraging texture in space, sparsity locations in DCT domain, and directional decomposition of gradients, the sampler structure consists of a combination of uniform, random, and nonuniform sampling strategies. For reconstruction, we model the recovery problem as a two-state cellular automaton to iteratively restore image with scalable windows from generation to generation. We demonstrate the recovery algorithm quickly converges after a few generations for an image with arbitrary degree of texture. For a given number of measurements, extensive experiments on standard image-sets, infra-red, and mega-pixel range imaging devices show that the proposed measurement matrix considerably increases the overall recovery performance, or equivalently decreases the number of sampled pixels for a specific recovery quality compared to random sampling matrix and Gaussian linear combinations employed by the state-of-the-art compressive sensing methods. In practice, the proposed measurement-adaptive sampling/recovery framework includes various applications from intelligent compressive imaging-based acquisition devices to computer vision and graphics, and image processing technology. Simulation codes are available online for reproduction purposes.

Research paper thumbnail of A New Scheme for Vision Based Flying Vehicle Detection Using Motion Flow Vectors Classification

2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009

... Alireza Behrad Electrical Eng ... Faculty, S. and R. Branch, Islamic Azad University Tehran, ... more ... Alireza Behrad Electrical Eng ... Faculty, S. and R. Branch, Islamic Azad University Tehran, Iran sabouri.s@srbiau.ac.ir ... In the applications with mobile camera, Behrad and Motamedi [12] introduced a statistical approach based on the planar Affine transformation for locating moving ...

Research paper thumbnail of A Novel Fit-Flexible Fluorescence Soft Imager: Tri-Sensing of Intensity, Fall-Time, and Life Profile

IEEE transactions on bio-medical engineering/IEEE transactions on biomedical engineering, 2024

Research paper thumbnail of Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: a Review

Methods and Applications in Fluorescence, Dec 5, 2023

Research paper thumbnail of Forensic discrimination between traditional and compressive imaging by blurring kernel investigation

Multimedia Tools and Applications, Oct 11, 2023

Research paper thumbnail of A novel fit-flexible fluorescence imager: Tri-sensing of intensity, fall-time, and life profile

The preprint presents a novel sensing approach to time-resolved fluorescence imaging. It is also ... more The preprint presents a novel sensing approach to time-resolved fluorescence imaging. It is also supported by supplementary materials.

Research paper thumbnail of Fast and Robust Single-Exponential Decay Recovery From Noisy Fluorescence Lifetime Imaging

IEEE Transactions on Biomedical Engineering

Research paper thumbnail of A part-level learning strategy for JPEG image recompression detection

Multimedia Tools and Applications, 2021

Research paper thumbnail of A novel forensic image analysis tool for discovering double JPEG compression clues

Multimedia Tools and Applications, 2016

This paper presents a novel technique to discover double JPEG compression traces. Existing detect... more This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool discriminates single compressed images from double counterparts, estimates the first quantization in double compression, and localizes tampered regions in a forgery examination. Extensive experiments on three databases demonstrate results are robust among different quality levels.

Research paper thumbnail of Quantization-Unaware Double JPEG Compression Detection

Journal of Mathematical Imaging and Vision, 2015

The current double JPEG compression detection techniques identify whether or not an JPEG image fi... more The current double JPEG compression detection techniques identify whether or not an JPEG image file has undergone the compression twice, by knowing its embedded quantization table. This paper addresses another forensic scenario in which the quantization table of a JPEG file is not explicitly or reliably known, which may compel the forensic analyst to blindly reveal the recompression clues. To do this, we first statistically analyze the theory behind quantized Alternating Current (AC) modes in JPEG compression and show that the number of quantized AC modes required to detect double compression is a function of both the image's block texture and the compression's quality level in a fresh formulation. Consequently, a new double compression detection algorithm is proposed that exploits footprints introduced by all non-zero and zero AC modes based on Benford's law in a low-dimensional representation via PCA. Then, some evaluation frameworks are constructed to assess the robustness and generalization of the proposed method on

Research paper thumbnail of A new deformable mesh model for face tracking using edge based features and novel sets of energy functions

Multimedia Tools and Applications, 2014

ABSTRACT This paper presents a new method for automatic human face locating and tracking. The pro... more ABSTRACT This paper presents a new method for automatic human face locating and tracking. The proposed method consists of two modules including face locating and face tracking. The face locating module has a hierarchical structure, which consists of a skin color classifier together with AdaBoost based face detectors. The face tracking module is considered to be the main contribution of the paper. The module is based on the unstructured 2-D triangular deformable meshes, which employs a new robust and illumination insensitive feature extraction and matching algorithms as well as new sets of mesh energy functions. The feature extraction and matching algorithms are established upon edge points and their representation using fuzzy set theory, which is called fuzzy edges. For matching features, a multiresolution algorithm is utilized based on fuzzy edges and edge pyramid. The new mesh energy functions are also employed to manage both rigid and non-rigid motions in the head and face. Experimental results demonstrate the accuracy and stability of the proposed method for both face locating and face tracking.

Research paper thumbnail of A proper transform for satisfying Benford's Law and its application to double JPEG image forensics

2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2012

This paper presents a new transform domain to evaluate the goodness of fit of natural image data ... more This paper presents a new transform domain to evaluate the goodness of fit of natural image data to the common Benford's Law. The evaluation is made by three statistical fitness criteria includ ing Pearson's chi-square test statistic, normalized cross correlation and a distance measure based on symmetrized Kullback-Leibler di vergence. It is shown that the serial combination of variance filtering and block 2-D discrete cosine transform reveals the best goodness of fit for the first significant digit. We also show that the proposed transform domain brings reasonable fit for the second, third and fourth significant digits. As an application, the proposed transform domain is utilized to detect image manipulation by distinguishing single compressed images from doubly compressed ones.

Research paper thumbnail of Vision based flying vehicle tracking

2008 IEEE International Conference on Systems, Man and Cybernetics, 2008

In this paper, we have proposed an efficient vision based method for the tracking of flying vehic... more In this paper, we have proposed an efficient vision based method for the tracking of flying vehicles via a moving video camera. The suggested approach is based on the combination of the feature extraction and matching algorithm as well as the 2D deformable mesh surfaces. We have developed a new set of energy functions for mesh to improve the performance

Research paper thumbnail of Automatic Human Face Detection and Tracking using Cascaded Classifiers and Deformable Meshes

sid.ir

In this paper, we propose a novel method for fully automatic detection and tracking of human head... more In this paper, we propose a novel method for fully automatic detection and tracking of human heads and faces in video sequences. The proposed algorithm consists of two modules: a face detection module and a face tracking module. The Detection module, detects the face region and approximates it with an ellipse at the first frame using a modified version of AdaBoost cascaded classifier. The detection module is capable of considering the 2-D head pose rotation. The tracking module utiliyes a combination of deformable mesh energy minimization and feature matching approaches. In order to track a face, features are extracted in the face region to tessellate the human face with triangular unstructured meshes. For tracking a mesh, it is necessary to define mesh energies including internal and external energies. We have used new energy definitions for both the internal and the external energies which can accurately track rigid and non-rigid motions of a face and facial features at subsequent frames. We tested the proposed method with different video samples like cluttered backgrounds, partial illumination variations, put on glasses, and 2-D and/or 3-D rotating and translating heads. The experimental results showed that the algorithm is rotation insensitive and has high accuracy, stability and also has convergence for face detection and tracking.

Research paper thumbnail of Measurement-Adaptive Sparse Image Sampling and Recovery

Cornell University - arXiv, Jun 9, 2017

This paper presents an adaptive and intelligent sparse model for digital image sampling and recov... more This paper presents an adaptive and intelligent sparse model for digital image sampling and recovery. In the proposed sampler, we adaptively determine the number of required samples for retrieving image based on space-frequency-gradient information content of image patches. By leveraging texture in space, sparsity locations in DCT domain, and directional decomposition of gradients, the sampler structure consists of a combination of uniform, random, and nonuniform sampling strategies. For reconstruction, we model the recovery problem as a two-state cellular automaton to iteratively restore image with scalable windows from generation to generation. We demonstrate the recovery algorithm quickly converges after a few generations for an image with arbitrary degree of texture. For a given number of measurements, extensive experiments on standard image-sets, infra-red, and mega-pixel range imaging devices show that the proposed measurement matrix considerably increases the overall recovery performance, or equivalently decreases the number of sampled pixels for a specific recovery quality compared to random sampling matrix and Gaussian linear combinations employed by the state-of-the-art compressive sensing methods. In practice, the proposed measurement-adaptive sampling/recovery framework includes various applications from intelligent compressive imaging-based acquisition devices to computer vision and graphics, and image processing technology. Simulation codes are available online for reproduction purposes.

Research paper thumbnail of An Image Dataset of Vehicles Front Views and Parts for Vehicle Detection, Localization and Alignment Applications

2020 10th International Symposium onTelecommunications (IST), 2020

This paper introduces a dataset of front view car images collected from traffic scenes. Each imag... more This paper introduces a dataset of front view car images collected from traffic scenes. Each image contains the bounding box of a vehicle which has cropped manually and accurately. The database also includes the parts corresponding to each frontal view car, i.e. left and right lights, left and right mirrors, grill, license plate, and bumper. In addition to vision-based applications such as object detection, verification, and part-based analysis, our database is specialized for testing and developing of trainable bounding box location prediction algorithms. We used a car localization algorithm for performance evaluation, which resulted in a localization accuracy of 87%. Also, we tested our dataset on Viola-Jones object detector and measured its performance in terms of false positive and false negative rates for images of whole-car as well as each individual part.

Research paper thumbnail of Forensic Discrimination between Traditional and Compressive Imaging Systems

ArXiv, 2018

Compressive sensing is a new technology for modern computational imaging systems. In comparison t... more Compressive sensing is a new technology for modern computational imaging systems. In comparison to widespread conventional image sensing, the compressive imaging paradigm requires specific forensic analysis techniques and tools. In this regards, one of basic scenarios in image forensics is to distinguish traditionally sensed images from sophisticated compressively sensed ones. To do this, we first mathematically and systematically model the imaging system based on compressive sensing technology. Afterwards, a simplified version of the whole model is presented, which is appropriate for forensic investigation applications. We estimate the nonlinear system of compressive sensing with a linear model. Then, we model the imaging pipeline as an inverse problem and demonstrate that different imagers have discriminative degradation kernels. Hence, blur kernels of various imaging systems have utilized as footprints for discriminating image acquisition sources. In order to accomplish the ident...

Research paper thumbnail of Adaptive Sparse Image Sampling and Recovery

IEEE Transactions on Computational Imaging, 2018

This paper presents an adaptive and intelligent sparse model for digital image sampling and recov... more This paper presents an adaptive and intelligent sparse model for digital image sampling and recovery. In the proposed sampler, we adaptively determine the number of required samples for retrieving image based on space-frequency-gradient information content of image patches. By leveraging texture in space, sparsity locations in DCT domain, and directional decomposition of gradients, the sampler structure consists of a combination of uniform, random, and nonuniform sampling strategies. For reconstruction, we model the recovery problem as a two-state cellular automaton to iteratively restore image with scalable windows from generation to generation. We demonstrate the recovery algorithm quickly converges after a few generations for an image with arbitrary degree of texture. For a given number of measurements, extensive experiments on standard image-sets, infra-red, and mega-pixel range imaging devices show that the proposed measurement matrix considerably increases the overall recovery performance, or equivalently decreases the number of sampled pixels for a specific recovery quality compared to random sampling matrix and Gaussian linear combinations employed by the state-of-the-art compressive sensing methods. In practice, the proposed measurement-adaptive sampling/recovery framework includes various applications from intelligent compressive imaging-based acquisition devices to computer vision and graphics, and image processing technology. Simulation codes are available online for reproduction purposes.

Research paper thumbnail of A New Scheme for Vision Based Flying Vehicle Detection Using Motion Flow Vectors Classification

2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009

... Alireza Behrad Electrical Eng ... Faculty, S. and R. Branch, Islamic Azad University Tehran, ... more ... Alireza Behrad Electrical Eng ... Faculty, S. and R. Branch, Islamic Azad University Tehran, Iran sabouri.s@srbiau.ac.ir ... In the applications with mobile camera, Behrad and Motamedi [12] introduced a statistical approach based on the planar Affine transformation for locating moving ...