In this paper we propose a novel texture recognition feature called Fractal Weighted Local Binary Pattern (FWLBP). It has been observed that fractal dimension (FD) measure is relatively invariant to scale-changes, and presents a good correlation with human perception of surface roughness. We have utilized this property to construct a scale-invariant descriptor. We have sampled the input image u...

Classifying color textures under varying illumination sources remains challenging. To address this issue, this paper introduces a new descriptor for color texture classification, which is robust to changes in the scene illumination. The proposed descriptor, named Color Intensity Local Mapped Pattern (CILMP), incorporates relevant information about the color and texture patterns from the image i...

Development of various image descriptors has greatly contributed to the advancement in image retrieval. Image descriptors can be made invariant to various image changes. In this paper, a new image descriptor is described and its application in image retrieval is evaluated. The descriptor uses the autoencoder concept to reduce the dimension of the feature vector measuing various properties of a ...

Image classification is one of the most useful and essential research field in computer vision domain and challenging task in the image management and retrieval system. The growing demands for image classification in computer vision having application such as video surveillance, image and video retrieval, web content analysis, biometrics etc. have pushed application developers to search and cla...

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. Such a descriptor--based on a set of oriented Gaussian derivative filters-is used in our recognition system. We report here an evaluation of several techniques for orientation estimation to achieve rotation invariance...

We propose a novel local image descriptor called the Extended Multi-resolution Local Patterns, and a discriminative probabilistic framework for learning its parameters together with a multi-class image classifier. Our approach uses training data with image-level labels to learn the features which are discriminative for multi-class colonoscopy image classification. Experiments on a three class (...

In image registration or matching, the feature extracted by using traditional methods does not include depth information which may lead to a mismatch of keypoints. this paper, we prove that when camera moves, ratio difference keypoint and its neighbor pixel before after movement approximates constant. That means normalization is invariant movement. Based on property, all differences pixels cons...

Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of irregularity of the medical images. This descriptor property does not give ownership of the local image structure. In this paper, we present a combination of this parameter based on Box ...

A Gabor wavelets based method is proposed in this paper for evaluating and tuning the parameters of image registration algorithms. We propose a 3D local anatomical structure descriptor, namely the Maximum Responded Gabor Wavelet (MRGW), for measuring registration quality based on anatomical variability of registered images. The effectiveness of the descriptor is demonstrated through a practical...

Tensor scale is a morphometric parameter that unifies the representation of local structure thickness, orientation, and anisotropy, which can be used in several computer vision and image processing tasks. In this paper, we exploit this concept for binary images and propose a shape descriptor that encodes region and contour properties in a very efficient way. Experimental results are provided, s...