In this paper, we propose an invariant object detection method based on evidence accumulation and the Gabor transforms feature. In contrast to conventional evidence accumulation methods, the proposed method uses Gabor transform features to detect object parts. Experimental results prove that our algorithm robustly detects arbitrary shaped objects in cluttered environments with invariance to tra...

This paper presents a novel texture description approach, which is robust to variances in rotation, scale and illumination in images, to classify the texture of images. A limitation with traditional methods is that they are more or less sensitive to the mentioned changes in images. To overcome this problem, we propose a novel Local Haar Binary Pattern (LHBP) based framework to ensure invariance...

Local Binary Patterns (LBPs) have been used in a wide range of texture classification scenarios and have proven to provide a highly discriminative feature representation. A major limitation of LBP is its sensitivity to affine transformations. In this work, we present a scale- and rotation-invariant computation of LBP. Rotation-invariance is achieved by explicit alignment of features at the extr...

Extracting local rotation invariant features is a popular method for the classification of rotation invariant texture. To address the issue of local rotation invariance, many algorithms based on anisotropic features were proposed. Usually a dominant orientation is found out first, and then anisotropic feature is extracted by this orientation. To validate whether local dominant orientation is ne...

Weakly electric fish use active electrolocation for object detection and orientation in their environment even in complete darkness. The African mormyrid Gnathonemus petersii can detect object parameters, such as material, size, shape, and distance. Here, we tested whether individuals of this species can learn to identify 3-dimensional objects independently of the training conditions and indepe...

In macaque inferotemporal cortex (IT), neurons have been found to respond selectively to complex shapes while showing broad tuning ("invariance") with respect to stimulus transformations such as translation and scale changes and a limited tuning to rotation in depth. Training monkeys with novel, paperclip-like objects, Logothetis et al. 9 could investigate whether these invariance properties ar...

Rotation-invariance is a desired property of machine-learning models for medical image analysis and in particular computational pathology applications. We propose framework to encode the geometric structure special Euclidean motion group SE(2) convolutional networks yield translation rotation equivariance via introduction SE(2)-group convolution layers. This enables learn feature representation...

This paper proposes a new image hashing method, which uses histogram reconstruction to solve the problem of not being sensitive change pixel position, while ensuring robustness algorithm against common content preservation attacks (such as blurring, noise addition and rotation). The proposed can resist arbitrary angles rotation, possibly because reconstructed leverages rotational symmetry its o...

Dynamic texture is an extension of texture to the temporal domain. Description and recognition of dynamic textures has attracted growing attention. In this paper, a new method for recognizing dynamic textures is proposed. The textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in still texture analysis, combining the motion and a...

The matching of two-dimensional shapes is an important problem with applications in domains as diverse as biometrics, industry, medicine and zoology. The distance measure used must be invariant to many distortions, including scale, offset, noise, partial occlusion, etc. Most of these distortions are relatively easy to handle, either in the representation of the data or in the similarity measure...