Curvature Scale Space Research Papers (original) (raw)

Top Papers
Most Cited Papers
Most Downloaded Papers
Newest Papers
People
- by and +1
- •
- Feature Extraction, Gray Level Co-occurrence Matrix, Texture Features, Species Description

A method for shape analysis of diatoms (single-cell al- gae with silica shells) based on extraction of features on the contour of the cells by multi-scale mathematical morphol- ogy is presented. After building a morphological contour curvature scale space, we present a method for extracting the most prominent features by unsupervised cluster anal- ysis. The number of extracted features matches

- by Jos B. T. M. Roerdink
- •
- Feature matching, Silica, Shape Analysis, Single Cell

There are many applications, such as image copyright protection, where transformed images of a given test image need to be identified. The solution to this identification problem consists of two main stages. In stage one, certain representative features, such as corners, are detected in all images. In stage two, the representative features of the test image and the stored images are compared to identify the transformed images for the test image. Curvature scale-space (CSS) corner detectors look for curvature maxima or inflection points on planar curves. However, the arc-length used to parameterize the planar curves by the existing CSS detectors is not invariant to geometric transformations such as scaling. As a solution to stage one, this paper presents an improved CSS corner detector using the affine-length parameterization which is relatively invariant to affine transformations. We then present an improved corner matching technique as a solution to the stage two. Finally, we apply the proposed corner detection and matching techniques to identify the transformed images for a given image and report the promising results.

- by Mohammad Awrangjeb
- •
- Image Processing, Curvature, Corner Detection, Curvature Scale Space
- by Alfons Salden
- •
- Computer Vision, Scale Space, Boolean Satisfiability, Curvature Scale Space

Advancement in digital technologies have resulted in large amount of images. Hence there a huge demand in retrieving desired images. In order to retrieve an image, the image has to be described or represented by certain features. Shape is an important visual feature of an image. Searching for images using shape features has been an important field of study. There are many shape representation and description techniques in the literature. In this paper, we review one of the important shape descriptor techniques named curvature scale space. The CSS extracts the shape feature of the image and represents them as curvature maxima points. We discuss implementation procedure of this technique, its variants and discuss their advantages and disadvantages.

- by Arjun Paramarthalingam
- •
- Shape Recognition, Curvature Scale Space
- by Miguel Castelo-Branco Sousa
- •
- Image segmentation, Databases, Segmentation, Diabetic Retinopathy
- by Riku Suomela
- •
- Computer Vision, Image Processing, Signal Processing, Machine Vision

Many contour-based image corner detectors are based on the curvature scale-space (CSS). We identify the weaknesses of the CSS-based detectors. First, the " curvature " itself by its " definition " is very much sensitive to the local variation and noise on the curve, unless an appropriate smoothing is carried out beforehand. In addition, the calculation of curvature involves derivatives of up to second order, which may cause instability and errors in the result. Second, the Gaussian smoothing causes changes to the curve and it is difficult to select an appropriate smoothing-scale, resulting in poor performance of the CSS corner detection technique. We propose a complete corner detection technique based on the chord-to-point distance accumulation (CPDA) for the discrete curvature estimation. The CPDA discrete curvature estimation technique is less sensitive to the local variation and noise on the curve. Moreover, it does not have the undesirable effect of the Gaussian smoothing. We provide a comprehensive performance study. Our experiments showed that the proposed technique performs better than the existing CSS-based and other related methods in terms of both average repeatability and local-ization error.

- by Pascal Monasse and +1
- •
- Scientific Visualization, Image Processing, Image Reconstruction, PARTIAL DIFFERENTIAL EQUATION
- by Stephen Juggins
- •
- Cognitive Science, Machine Vision, Silica, Decision Tree
- by Said Mahmoudi
- •
- Cognitive Science, Pattern Recognition, Vote, Segmentation
- by Dirk Smeets
- •
- Face Recognition, Local Features, Mean Average Precision, Curvature Scale Space

- by Arjun Paramarthalingam
- •
- Computer Science, Curvature, Shape Recognition, Curvature Scale Space
- by Miguel Castelo-Branco Sousa
- •
- Image segmentation, Databases, Segmentation, Diabetic Retinopathy
- by Ricardo Torres
- •
- Image Processing, Image Analysis, Content based image retrieval, Image Retrieval

The great biodiversity of species makes the plants classification a very complex and time-consuming task. The leaf is an important characteristic of the plant and it is present independently of season or plant maturity. The most relevant information about the leaf relies on shape. Its study enables us to discriminate a large set of species and to speed up the measures extraction process, which is traditionally performed manually. This paper presents a novel approach to leaf shape identification based on curvature complexity analysis. By using the Curvature Scale Space (CSS), a curve describing the complexity of the shape is achieved. Descriptors computed from this curve are used to classify a set of leaves shapes. Results demonstrate the potential of the technique, which overcome traditional shape analysis methods found in literature.

- by André Backes
- •
- Complexity, Seasonality, Curvature, Shape Analysis
- by Marco Zuliani
- •
- Higher Order Thinking, Shape Descriptor, Curvature Scale Space, Rotation Invariance
- by Daniel Valdes
- •
- Shape, Statistical Shape Modelling, Local Shape Models, Curvature Scale Space
- by Mirek Bober
- •
- Information Systems, Mathematics, Computer Science, Computer Vision
- by Daniel Alejandro Valdes Amaro
- •
- Mathematics, Computer Science, Shape Modeling, Key words
- by Otman Basir
- •
- Mathematics, Computer Science, Content based image retrieval, Image Retrieval
- by Maher Ahmed
- •
- Pattern Recognition, Image Analysis, Wavelet Transform, Curvature
- by Alfons Salden
- •
- Computer Vision, Curvature, Connection, Scale Space
- by Alfons Salden
- •
- Computer Vision, Curvature, Connection, Scale Space

With the advances in digital imagery, large accessible data storage, internet repositories, and image applications, information conveyed through images is gaining in importance. There is a need to find a desired image from a collection of images, which is shared by many groups including journalists, engineers, historians, designers, teachers, artists and advertising agencies. Humans have the capability to determine only

- by Bee Khoo
- •
- Parameter estimation, System Approach, Image Retrieval, Data storage

Motion trajectories provide rich spatio-temporal information about an object's activity. The trajectory information can be obtained using a tracking algorithm on data streams available from a range of devices including motion sensors, video cameras, haptic devices, etc. Developing view-invariant activity recognition algorithms based on this high dimensional cue is an extremely challenging task. This paper presents efficient activity recognition algorithms using novel view-invariant representation of trajectories. Towards this end, we derive two Affine-invariant representations for motion trajectories based on curvature scale space (CSS) and centroid distance function (CDF). The properties of these schemes facilitate the design of efficient recognition algorithms based on hidden Markov models (HMMs). In the CSS-based representation, maxima of curvature zero crossings at increasing levels of smoothness are extracted to mark the location and extent of concavities in the curvature. The sequences of these CSS maxima are then modeled by continuous density (HMMs). For the case of CDF, we first segment the trajectory into subtrajectories using CDF-based representation. These subtrajectories are then represented by their Principal Component Analysis (PCA) coefficients. The sequences of these PCA coefficients from subtrajectories are then modeled by continuous density hidden Markov models (HMMs). Different classes of object motions are modeled by one Continuous HMM per class where state PDFs are represented by GMMs. Experiments using a database of around 1750 complex trajectories (obtained from UCI-KDD data archives) subdivided into five different classes are reported.

- by Òscar Chic
- •
- Genetics, Physiology, Oceanography, Biogeochemistry
- by Maher Ahmed
- •
- Image, Wavelet Transform, Curvature Scale Space, Shape Representation

The recognition of objects that appear in a video sequence is an essential aspect of any video content analysis system. We present an approach which classifies a segmented video object base don its appearance in successive video frames.... more

- by Stephan Kopf
- •
- Video Object, Curvature Scale Space

Curvature scale-space (CSS) corner detectors look for curvature maxima or inflection points on planar curves. They use arc-length parameterized curvature. Therefore, they are not robust to affine transformations since the arc-length of a curve is not preserved under affine transformations. However, the affine-length of a curve is relatively invariant to affine transformations. This paper presents an improved CSS corner detector by applying the affine-length parameterized curvature to the CSS corner detection technique. A thorough robustness study has been carried out on a large database considering a wide range of affine transformations.

- by Mohammad Awrangjeb
- •
- Corner Detection, Curvature Scale Space, Affine Transformation