A new two-dimensional block adaptive FIR filtering algorithm and its application to image restoration

Two-dimensional polar harmonic transforms for invariant image representation

This paper introduces a set of 2D transforms, based on a set of orthogonal projection bases, to generate a set of features which are invariant to rotation. We call these transforms Polar Harmonic Transforms (PHTs). Unlike the well-known Zernike and pseudo-Zernike moments, the kernel computation of PHTs is extremely simple and has no numerical stability issue whatsoever. This implies that PHTs encompass the orthogonality and invariance advantages of Zernike and pseudo-Zernike moments, but are free from their inherent limitations. This also means that PHTs are well suited for application where maximal discriminant information is needed. Furthermore, PHTs make available a large set of features for further feature selection in the process of seeking for the best discriminative or representative features for a particular application.

Computer-aided detection of bleeding regions for capsule endoscopy images

Capsule endoscopy (CE) has been widely used to diagnose diseases in human digestive tract. However, a tough problem of this new technology is that too many images to be inspected by eyes cause a huge burden to physicians, so it is significant to investigate computerized diagnosis methods. In this paper, a new computer-aided system aimed for bleeding region detection in CE images is proposed. This new system exploits color texture feature, an important clue used by physicians, to analyze status of gastrointestinal tract. We put forward a new idea of chrominance moment as the color part of color texture feature, which makes full use of Tchebichef polynomials and illumination invariant of hue/saturation/intensity color space. Combined with uniform local binary pattern, a current texture representation model, it can be applied to discriminate normal regions and bleeding regions in CE images. Classification of bleeding regions using multilayer perceptron neural network is then deployed to verify performance of the proposed color texture features. Experimental results on our bleeding image data show that the proposed scheme is promising in detecting bleeding regions.

Rotation moment invariants for recognition of symmetric objects

In this paper, a new set of moment invariants with respect to rotation, translation, and scaling suitable for recognition of objects having N-fold rotation symmetry are presented. Moment invariants described earlier cannot be used for this purpose because most moments of symmetric objects vanish. The invariants proposed here are based on complex moments. Their independence and completeness are proven theoretically and their performance is demonstrated by experiments.

Rotation-invariant multiresolution texture analysis using radon and wavelet transforms

A new rotation-invariant texture-analysis technique using Radon and wavelet transforms is proposed. This technique utilizes the Radon transform to convert the rotation to translation and then applies a translation-invariant wavelet transform to the result to extract texture features. A kappa-nearest neighbors classifier is employed to classify texture patterns. A method to find the optimal number of projections for the Radon transform is proposed. It is shown that the extracted features generate an efficient orthogonal feature space. It is also shown that the proposed features extract both of the local and directional information of the texture patterns. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. To test and evaluate the method, we employed several sets of textures along with different wavelet bases. Experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture-analysis methods.