New approaches to texture coding in segmentation and feature-based image coding schemes

Peer ReviewedPostprint (published version

Rotation and Scale Invariant Texture Classification

Texture classification is very important in image analysis. Content based image retrieval, inspection of surfaces, object recognition by texture, document segmentation are few examples where texture classification plays a major role. Classification of texture images, especially those with different orientation and scale changes, is a challenging and important problem in image analysis and classification. This thesis proposes an effective scheme for rotation and scale invariant texture classification. The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So, the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O (n*log n) complexity. The experimental results, based on different testing data sets for images from Brodatz album with different orientations and scales, show that the implemented classification scheme outperforms other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 87.09 percent

Application of Shape-Independent Orthogonal Transforms for Image Interpolation

In the contribution we develop a new method for object-oriented interpolation of images. It is an important tool in image processing, since using interpolation we can considerably decrease amount of data, necessary for image reconstruction. Application of this interpolation enables to down-sample the object separately. The selected object can be processed at the different sampling level. This approach allows object oriented zoom, for example. Moreover, object - oriented approach is a very novel idea that helps to understand the content of image. Method is created from cosine approximation implemented to coding with shape - independent basis functions

Tumor Prediction in Mammogram using Neural Network

Detecting micro calcifications - early breast cancer indicators 2013; is visually tough while recognizing malignant tumors is a highly complicated issue. Digital mammography ensures early breast cancer detection through digital mammograms locating suspicious areas with benign/- malignant micro calcifications. Early detection is vital in treatment and survival of breast cancer as there are no sure ways to prevent it. This paper presents a method of tumor prediction based on extracting features from mammogram using Gabor filter with Discrete cosine transform and classify the features using Neural Network

Texture representation using wavelet filterbanks

Texture analysis is a fundamental issue in image analysis and computer vision. While considerable research has been carried out in the texture analysis domain, problems relating to texture representation have been addressed only partially and active research is continuing. The vast majority of algorithms for texture analysis make either an explicit or implicit assumption that all images are captured under the same measurement conditions, such as orientation and illumination. These assumptions are often unrealistic in many practical applications;This dissertation addresses the viewpoint-invariance problem in texture classification by introducing a rotated wavelet filterbank. The proposed filterbank, in conjunction with a standard wavelet filterbank, provides better freedom of orientation tuning for texture analysis. This allows one to obtain texture features that are invariant with respect to texture rotation and linear grayscale transformation. In this study, energy estimates of channel outputs that are commonly used as texture features in texture classification are transformed into a set of viewpoint-invariant features. Texture properties that have a physical connection with human perception are taken into account in the transformation of the energy estimates;Experiments using natural texture image sets that have been used for evaluating other successful approaches were conducted in order to facilitate comparison. We observe that the proposed feature set outperformed methods proposed by others in the past. A channel selection method is also proposed to minimize the computational complexity and improve performance in a texture segmentation algorithm. Results demonstrating the validity of the approach are presented using experimental ultrasound tendon images

Image segmentation with adaptive region growing based on a polynomial surface model

A new method for segmenting intensity images into smooth surface segments is presented. The main idea is to divide the image into flat, planar, convex, concave, and saddle patches that coincide as well as possible with meaningful object features in the image. Therefore, we propose an adaptive region growing algorithm based on low-degree polynomial fitting. The algorithm uses a new adaptive thresholding technique with the L∞ fitting cost as a segmentation criterion. The polynomial degree and the fitting error are automatically adapted during the region growing process. The main contribution is that the algorithm detects outliers and edges, distinguishes between strong and smooth intensity transitions and finds surface segments that are bent in a certain way. As a result, the surface segments corresponding to meaningful object features and the contours separating the surface segments coincide with real-image object edges. Moreover, the curvature-based surface shape information facilitates many tasks in image analysis, such as object recognition performed on the polynomial representation. The polynomial representation provides good image approximation while preserving all the necessary details of the objects in the reconstructed images. The method outperforms existing techniques when segmenting images of objects with diffuse reflecting surfaces

MULTIRIDGELETS FOR TEXTURE ANALYSIS

Directional wavelets have orientation selectivity and thus are able to efficiently represent highly anisotropic elements such as line segments and edges. Ridgelet transform is a kind of directional multi-resolution transform and has been successful in many image processing and texture analysis applications. The objective of this research is to develop multi-ridgelet transform by applying multiwavelet transform to the Radon transform so as to attain attractive improvements. By adapting the cardinal orthogonal multiwavelets to the ridgelet transform, it is shown that the proposed cardinal multiridgelet transform (CMRT) possesses cardinality, approximate translation invariance, and approximate rotation invariance simultaneously, whereas no single ridgelet transform can hold all these properties at the same time. These properties are beneficial to image texture analysis. This is demonstrated in three studies of texture analysis applications. Firstly a texture database retrieval study taking a portion of the Brodatz texture album as an example has demonstrated that the CMRT-based texture representation for database retrieval performed better than other directional wavelet methods. Secondly the study of the LCD mura defect detection was based upon the classification of simulated abnormalities with a linear support vector machine classifier, the CMRT-based analysis of defects were shown to provide efficient features for superior detection performance than other competitive methods. Lastly and the most importantly, a study on the prostate cancer tissue image classification was conducted. With the CMRT-based texture extraction, Gaussian kernel support vector machines have been developed to discriminate prostate cancer Gleason grade 3 versus grade 4. Based on a limited database of prostate specimens, one classifier was trained to have remarkable test performance. This approach is unquestionably promising and is worthy to be fully developed

Automatic Main Road Extraction from High Resolution Satellite Imagery

Road information is essential for automatic GIS (geographical information system) data acquisition, transportation and urban planning. Automatic road (network) detection from high resolution satellite imagery will hold great potential for significant reduction of database development/updating cost and turnaround time. From so called low level feature detection to high level context supported grouping, so many algorithms and methodologies have been presented for this purpose. There is not any practical system that can fully automatically extract road network from space imagery for the purpose of automatic mapping. This paper presents the methodology of automatic main road detection from high resolution satellite IKONOS imagery. The strategies include multiresolution or image pyramid method, Gaussian blurring and the line finder using 1-dimemsional template correlation filter, line segment grouping and multi-layer result integration. Multi-layer or multi-resolution method for road extraction is a very effective strategy to save processing time and improve robustness. To realize the strategy, the original IKONOS image is compressed into different corresponding image resolution so that an image pyramid is generated; after that the line finder of 1-dimemsional template correlation filter after Gaussian blurring filtering is applied to detect the road centerline. Extracted centerline segments belong to or do not belong to roads. There are two ways to identify the attributes of the segments, the one is using segment grouping to form longer line segments and assign a possibility to the segment depending on the length and other geometric and photometric attribute of the segment, for example the longer segment means bigger possibility of being road. Perceptual-grouping based method is used for road segment linking by a possibility model that takes multi-information into account; here the clues existing in the gaps are considered. Another way to identify the segments is feature detection back-to-higher resolution layer from the image pyramid