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Journal ArticleDOI

Weber local descriptor for image analysis and recognition: a survey

TLDR
Using WLD, the different challenges of image analysis and recognition features with respect to illumination changes, contrast differences, and geometrical transformations like rotation, scaling, translation, and mirroring are addressed.
Abstract
Weber local descriptor (WLD) is applied for addressing the challenges in image/pattern problems, especially in computer vision and pattern recognition domains. In this paper, we review literature on theories and applications of WLD. Using WLD, we address the different challenges of image analysis and recognition features with respect to illumination changes, contrast differences, and geometrical transformations like rotation, scaling, translation, and mirroring. Further, the role of the classifiers and experimental protocols used in the different applications are discussed. Applications include texture classification, medical imaging, agricultural safety, fingerprint analysis, forgery analysis, and face recognition.

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Citations
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Journal ArticleDOI

A robust edge detection algorithm based on feature-based image registration (FBIR) using improved canny with fuzzy logic (ICWFL)

TL;DR: A feature-based image registration (FBIR) method in combination with an improved version of canny with fuzzy logic is proposed for accurate detection of edges in various well-known image databases.
Journal ArticleDOI

Secure and efficient privacy protection system for medical records

TL;DR: In this article , the authors presented a BioHashing and watermarking-based technique capable of providing integrity, authenticity, and confidentiality to different medical images, which meets all the requirements of robustness and imperceptibility.
Journal ArticleDOI

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

TL;DR: This paper discusses and presents a micro-pattern descriptor, called Local Directional Number Pattern along with Multi-scale Weber Local Descriptor for feature extraction task to determine cancerous and noncancerous blood cells.
References
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Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Proceedings ArticleDOI

Rapid object detection using a boosted cascade of simple features

TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Journal ArticleDOI

Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Journal ArticleDOI

Robust Real-Time Face Detection

TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Proceedings ArticleDOI

Robust real-time face detection

TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.