Detection Of Image Regions Research Articles

Individualized image region detection with total variation

AbstractMedical image data have emerged to be an indispensable component of modern medicine. Different from many general image problems that focus on outcome prediction or image recognition, medical image analysis pays more attention to model interpretation. For instance, given a list of medical images and corresponding labels of patients' health status, it is often of greater importance to identify the image regions that could differentiate the outcome status, compared to simply predicting labels of new images. Moreover, medical image data often demonstrate strong individual heterogeneity. In other words, the image regions associated with an outcome could be different across patients. As a consequence, the traditional one‐model‐fits‐all approach not only omits patient heterogeneity but also possibly leads to misleading or even wrong conclusions. In this article, we introduce a novel statistical framework to detect individualized regions that are associated with a binary outcome, that is, whether a patient has a certain disease or not. Moreover, we propose a total variation‐based penalization for individualized image region detection under a local label‐free scenario. Considering that local labeling is often difficult to obtain for medical image data, our approach may potentially have a wider range of applications in medical research. The effectiveness of our proposed approach is validated by two real histopathology databases: Colon Cancer and Camelyon16.

Detection of leaf structures in close-range hyperspectral images using morphological fusion

Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods.

A Spectral and Spatial Approach of Coarse-to-Fine Blurred Image Region Detection

Blur exists in many digital images, it can be mainly categorized into two classes: defocus blur which is caused by optical imaging systems and motion blur which is caused by the relative motion between camera and scene objects. In this letter, we propose a simple yet effective automatic blurred image region detection method. Based on the observation that blur attenuates high-frequency components of an image, we present a blur metric based on the log averaged spectrum residual to get a coarse blur map. Then, a novel iterative updating mechanism is proposed to refine the blur map from coarse to fine by exploiting the intrinsic relevance of similar neighbor image regions. The proposed iterative updating mechanism can partially resolve the problem of differentiating an in-focus smooth region and a blurred smooth region. In addition, our iterative updating mechanism can be integrated into other image blurred region detection algorithms to refine the final results. Both quantitative and qualitative experimental results demonstrate that our proposed method is more reliable and efficient compared to various state-of-the-art methods.

A dense subgraph based algorithm for compact salient image region detection

We present an algorithm for graph based saliency computation that utilizes the underlying dense subgraphs in finding visually salient regions in an image. To compute the salient regions, the model first obtains a saliency map using random walks on a Markov chain. Next, k-dense subgraphs are detected to further enhance the salient regions in the image. Dense subgraphs convey more information about local graph structure than simple centrality measures. To generate the Markov chain, intensity and color features of an image in addition to region compactness is used. For evaluating the proposed model, we do extensive experiments on benchmark image data sets. The proposed method performs comparable to well-known algorithms in salient region detection.

Extraction of stable foreground image regions for unattended luggage detection

A novel approach to detection of stationary objects in the video stream is presented. Stationary objects are these separated from the static background, but remaining motionless for a prolonged time. Extraction of stationary objects from images is useful in automatic detection of unattended luggage. The proposed algorithm is based on detection of image regions containing foreground image pixels having stable values in time and checking their correspondence with the detected moving objects. In the first stage of the algorithm, stability of individual pixels belonging to moving objects is tested using a model constructed from vectors. Next, clusters of pixels with stable color and brightness are extracted from the image and related to contours of the detected moving objects. This way, stationary (previously moving) objects are detected. False contours of objects removed from the background are also found and discarded from the analysis. The results of the algorithm may be analyzed further by the classifier, separating luggage from other objects, and the decision system for unattended luggage detection. The main focus of the paper is on the algorithm for extraction of stable image regions. However, a complete framework for unattended luggage detection is also presented in order to show that the proposed approach provides data for successful event detection. The results of experiments in which the proposed algorithm was validated using both standard datasets and video recordings from a real airport security system are presented and discussed.

Pornographic image region detection based on visual attention model in compressed domain

According to biological attention mechanism, a region of interest (ROI) detection based on visual attention model is closer to human visual system. Taken into account the characteristics of pornographic image during regions detection, a pornographic image region detection method based on visual attention model in compressed domain is proposed in this study, which includes the following four steps: (i) the skin colour regions of pornographic images are detected in compressed domain; (ii) visual saliency map in compressed domain is computed to construct visual attention model; (iii) threshold segmentation method is used for visual saliency map, and then the torso information is retained as pornographic regions; and (iv) four features of colour, texture, intensity and skin are extracted to represent pornographic region. The experimental results show that the proposed method can perform well on the speed/accuracy of pornographic regions detection and representation.

An Improved Computational Approach for Salient Region Detection

Salient region detection in images is very useful for image processing applications like image compressing, image segmentation, object detection and recognition. In this paper, an improved approach to detect salient region is presented . The proposed method can generate a robust saliency map and extract salient regions with precise boundaries. In the proposed method, local saliency, global saliency and rarity saliency of three kinds of low-level feature contrast of intensity, color and orientation are used to compute the visual saliency. A new feature integration strategy is proposed in this paper. This method can select features and compute the weights of the features dynamically by analyzing the effect of different features on the saliency. Then a more robust saliency map is obtained. It has been tested on many images to evaluate the validity and effectiveness of the proposed method. We also compare our method with other salient region detection methods and our method outperforms other methods in detection results.

Affine image region detection and description

This work has been partially granted by the Spanish Ministerio de Ciencia e Innovación (MCINN) and FEDER funds, and by Junta de Andalucía, under projects no. TIN2008-06196 and P07-TIC-03106, respectively.

Multi-Channel Filtering Approach for Unsupervised Segmentation of Subsurface Radar Images

The volume of image data generated in ground-penetrating radar surveys can severely restrict the practicality of this site investigation technique. This is particularly true in situations where automatic analysis or interpretation is required, as segmentation and classification tasks that utilise multivariate data are critically affected by the volume and dimensionality of the data. A general-purpose unsupervised image segmentation system is presented here for the automatic detection of image regions exhibiting different visual texture properties. A bank of Gabor filters is used to achieve multi-channel filtering analogous to the processing of information in the visual cortex. A suboptimal feature-selection procedure is proposed to automatically select the set of texture features best suited for the particular application. The reduction in the size of the feature set both reduces the computation time and improves the accuracy of the final classification. Unsupervised neural networks are employed to segment and classify the image data into categories. Results presented for two typical radargrams match closely the interpretation of a human expert, in the fraction of the time. Applications of this technique can be found in disciplines as diverse as medical image analysis and ultrasonic non-destructive testing.

Automatic feature selection for unsupervised image segmentation

A computational bottleneck is often imposed by the volume of image data generated in disciplines such as remote sensing and medical imaging, especially in situations where automatic analysis or interpretation is required. Segmentation and classification tasks that utilize multivariate data can be impeded by this dimensionality. A general-purpose unsupervised image segmentation system is presented here for the automatic detection of image regions exhibiting different visual texture properties. A suboptimal feature selection procedure is proposed to automatically select the set of texture features best suited for the particular application. Results are presented for the segmentation of ground-penetrating radar images for generating automatic subsurface reports. The reduction in the size of the feature set both reduces the computation time and improves the accuracy.