Perceptual-based textures for scene labeling: a bottom-up and a top-down approach

Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label

Lane Discovery in Traffic Video

Video sensing has become very important in Intelligent Transportation Systems (ITS) due to its relative low cost and non-invasive deployment. An effective ITS requires detailed traffic information, including vehicle volume counts for each lane in surveillance video of a highway or an intersection. The multiple-target, vehicle-tracking and counting problem is most reliably solved in a reduced space defined by the constraints of the vehicles driving within lanes. This requires lanes to be pre-specified. An off-line pre-processing method is presented which automatically discovers traffic lanes from vehicle motion in uncalibrated video from a stationary camera. A moving vehicle density map is constructed, then multiple lane curves are fitted. Traffic lanes are found without relying on possibly noisy tracked vehicle trajectories

ANALYSIS OF MAMMOGRAM FOR DETECTION OF BREAST CANCER USING WAVELET STATISTICAL FEATURES

Early detection of breast cancer increases the survival rate and increases the treatment options. One of the most powerful techniques for early detection of breast cancer is based on digital mammogram. A system can be developed for assisting the analysis of digital mammograms using log-Gabor wavelet statistical features. The proposed system involves three major steps called Pre-processing, Processing, and Feature extraction. In pre-processing, the digital mammogram can be de-noised using efficient decision-based algorithm. In processing stage, the suspicious Region of Interest (ROI) can be cropped and convolved with log-Gabor filter for four different orientations. Then gray level co-occurrence matrix (GLCM)can be constructed for log-Gabor filter output at four different orientations and from that first order statistical features and second order statistical features can be extracted to analyze whether the mammogram as normal or benign or malignant. The proposed method can allow the radiologist to focus rapidly on the relevant parts of the mammogram and it can increase the effectiveness and efficiency of radiology clinics

Perancangan Dan Implementasi Perangkat Lunak Sistem Pencocokan Sidik Jari Dengan Algoritma Filterbank Gabor

From research result used filterbank gabor algorithms, can give 96 (24 x 4) featurres from 24 sector with 4 filter. Gabor filter use 23 x 23 with orientation change 00, 450, 900, dan 1350. Each orientation angle change will be rotation gabor filter on to its. Make feature vector or FingerCode with Avarage Absolute Deviation (AAD) are average value from difference pixels number for each sector and centre value on sector. Error system on disjoint between FAR dan FRR with value 3,6% and threshold value 39. Then slope from GAR graphic value 400, this is proving that this system is running a good, cause slope recommendation value 450

Discrimination of Textures Using Texton Patterns

Textural patterns can often be used to recognize familiar objects in an image or retrieve images with similar texture from a database. Texture patterns can provide significant and abundance of texture and shape information. One of the recent significant and important texture features called Texton represents the various patterns of image which is useful in texture analysis. The present paper is an extension of our previous paper [1]. The present paper divides the 3 D7; 3 neighbourhood into two different 2 D7; 2 neighbourhood grids each consist four pixels. On this 2 D7; 2 grids shape descriptor indexes (SDI) are evaluated separately and added to form a Total Shape Descriptor Index Image (TSDI). By deriving textons on TSDI image Total Texton Shape Matrix (TTSM) image is formed and Grey Level Co-Occurence Matrix (GLCM) parameters are derived on it for efficient texture discrimination. The experimental result shows the efficacy of the present metho