Image category recognition is important to access visual information on the level of objects and scene types. In this paper, we propose a new approach for color object recognition using the powerful information provided by the color. This approach is based on the combination of Gray-Edge color constancy, hue components in HSV (hue, saturation, value) color space and cell and bin ideas used in the HOG (Histograms of Gradients) descriptors. The proposed oriented descriptor benefits of the invariance of hues against light intensity change, light intensity shift and light intensity change and shift, and solve its missing of invariance against light color change by using Gray-Edge color constancy. Moreover, the use of cells and bins in this proposed descriptor building boost its invariance the geometric and photo-metric transformation and increases the recognition rate. SVM classifiers (Support Vector Machine) which is a strong classification method known for its flexibility and its power of generalization are used for the training and recognition steps. The proposed method is evaluated on two publicly available datasets including Columbia Object Image Library and The Amsterdam Library of Object Images and obtained a recognition rate of 95.64% and 96.48% - clearly showing the exceptional performance compared to existing methods.

This paper presents a speech encryption scheme by performing a combination of modified chaotic maps inspired by classic logistic and cubic maps. The main idea was to enhance the performance of classical chaotic maps by extending the range of the chaotic parameter. The resulted combining map was applied to a speech encryption scheme by using the confusion and diffusion architecture. The evaluation results showed a good performance regarding the chaotic behaviors such as initial value, control parameter, Lyapunov exponent, and bifurcation diagram. Simulations and computer evaluations with security analysis showed that the proposed chaotic system exhibits excellent performance in speech encryption against various attacks. The results obtained demonstrated the efficiency of the proposed scheme compared to an existing valuable method for static and differential cryptographic attacks.

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In the last few years, there has been a lot of interest in making smart components, e.g. robots, able to simulate human capacity of object recognition and categorization. In this paper, we propose a new revolutionary approach for object categorization based on combining the HOG (Histograms of Oriented Gradients) descriptors with our two new descriptors, HOH (Histograms of Oriented Hue) and HOS (Histograms of Oriented Saturation), designed it in the HSL (Hue, Saturation and Luminance) color space and inspired by this famous HOG descriptor. By using the chrominance components, we have succeeded in making the proposed descriptor invariant to all lighting conditions changes. Moreover, the use of this oriented gradient makes our descriptor invariant to geometric condition changes including geometric and photometric transformation. Finally, the combination of color and gradient information increase the recognition rate of this descriptor and give it an exceptional performance compared to existing methods in the recognition of colored handmade objects with uniform background (98.92% for Columbia Object Image Library and 99.16% for the Amsterdam Library of Object Images). For the classification task, we propose the use of two strong and very used classifiers, SVM (Support Vector Machine) and KNN (k-nearest neighbors) classifiers.

In this paper, an improved Speckle Reducing Anisotropic Diffusion (SRAD), destined to remove multiplicative gamma noise applied to different images is proposed. The basic idea is to divide the image into several riddled areas and then calculate the Equivalent Number of Look (ENL) of each region. The largest value of the ENL is the best optimal homogeneous region of the image. This optimal choice allows us to solve the major problem of the SRAD algorithm articulated around a visual choice of the homogeneous region which is not satisfactory and causes non-uniformity in this area. To give more validity to the proposed method, several experimentations were conducted using different kinds of images and were approved by some quantitative metrics like PSNR, SNR, VSNR, and SSIM. The computer simulation results confirm the efficiency of the proposed method which outperformances the classical SRAD method.