Automatic Detection Of Regions Research Articles

A Study of Lighting Models for Automatic Detection of Regions of Interest and Surface Defects in Cast Metal Parts

A Study of Lighting Models for Automatic Detection of Regions of Interest and Surface Defects in Cast Metal Parts

Automatic Detection of Regions of Interest in Makler Images by Combinational Approach and Sperms Analysis by Fuzzy C-Means

Automatic Detection of Regions of Interest in Makler Images by Combinational Approach and Sperms Analysis by Fuzzy C-Means

Joint Multi-Image Saliency Analysis for Region of Interest Detection in Optical Multispectral Remote Sensing Images

The automatic detection of regions of interest (ROI) is useful for remote sensing image analysis, such as land cover classification, object recognition, image compression, and various computer vision related applications. Recently, approaches based on visual saliency have been utilized for ROI detection. However, most existing methods focus on detecting ROIs from a single image, which generally cannot precisely extract ROIs against a complicated background or exclude images with no ROIs. In this paper, we propose a joint multi-image saliency (JMS) algorithm to simultaneously extract the common ROIs in a set of optical multispectral remote sensing images with the additional ability to identify images that do not contain the common ROIs. First, bisecting K-means clustering on the entire image set allows us to extract the global correspondence among multiple images in RGB and CIELab color spaces. Second, clusterwise saliency computation aggregating global color and shape contrast efficiently assigns common ROIs with high saliency, while effectively depressing interfering background that is salient only within its own image. Finally, binary ROI masks are generated by thresholding saliency maps. In addition, we construct an edge-preserving JMS model through edge-preserving mask optimization strategy, so as to facilitate the generation of a uniformly highlighted ROI mask with sharp borders. Experimental results demonstrate the advantages of our model in detection accuracy consistency and runtime efficiency.

Automatic detection of regions of interest in breast ultrasound images based on local phase information.

Due to the inherent speckling and low contrast of ultrasonic images, the accurate and efficient location of regions of interest (ROIs) is still a challenging task for breast ultrasound (BUS) computer-aided diagnosis (CAD) systems. In this paper, a fully automatic and efficient ROI generation approach is proposed. First, a BUS image is preprocessed to improve image quality. Second, a phase of max-energy orientation (PMO) image of the preprocessed image is calculated. Otsu's threshold selection method is then used to binarize the preprocessed image, the phase image and the composed image obtained by adding and normalizing the set of two images. Finally, a region selection algorithm is developed to select the true tumor region from these three binary images before generating a final ROI. The method was validated on a BUS database with 168 cases (81 benign and 87 malignant); the accuracy, average precision rate and average recall rate are calculated and compared with conventional method. The results indicate that the proposed method is more accurate and efficient in locating ROIs.

Optimized 3D Street Scene Reconstruction from Driving Recorder Images

The paper presents an automatic region detection based method to reconstruct street scenes from driving recorder images. The driving recorder in this paper is a dashboard camera that collects images while the motor vehicle is moving. An enormous number of moving vehicles are included in the collected data because the typical recorders are often mounted in the front of moving vehicles and face the forward direction, which can make matching points on vehicles and guardrails unreliable. Believing that utilizing these image data can reduce street scene reconstruction and updating costs because of their low price, wide use, and extensive shooting coverage, we therefore proposed a new method, which is called the Mask automatic detecting method, to improve the structure results from the motion reconstruction. Note that we define vehicle and guardrail regions as “mask” in this paper since the features on them should be masked out to avoid poor matches. After removing the feature points in our new method, the camera poses and sparse 3D points that are reconstructed with the remaining matches. Our contrast experiments with the typical pipeline of structure from motion (SfM) reconstruction methods, such as Photosynth and VisualSFM, demonstrated that the Mask decreased the root-mean-square error (RMSE) of the pairwise matching results, which led to more accurate recovering results from the camera-relative poses. Removing features from the Mask also increased the accuracy of point clouds by nearly 30%–40% and corrected the problems of the typical methods on repeatedly reconstructing several buildings when there was only one target building.

Finding region of interest in the infrared image of electrical installation

This paper presents a method of automatically finding the region of interests (ROIs) in an infrared image of electrical installations. These regions are very important, particularly for diagnosing the thermal condition of electrical equipment. For a vast number of electrical equipment to be inspected, manual region selection of the images normally will take a lot of time. Therefore, an automatic region detection system is more preferred. However, due to the nature of the infrared image, the conventional segmentation methods have some limitations in order to properly find the desired ROIs. In addition, all objects within the image commonly have heterogeneous pixel intensities causing the segmented regions tend to be over segmented or some parts of the target objects either be divided into multiple regions or merging with the background image. Therefore, this paper proposes a new segmentation method of detecting the repeated structures of electrical equipment within an infrared image by taking the advantage of local keypoint feature matching. Experimental results indicate that the proposed method achieves a better performance for detecting the target ROIs compared with the conventional methods. The algorithm was tested on real infrared images with diverse irregular intensity variations and cluttered background. Finally, the performance of the proposed method was qualitatively and quantitatively evaluated.

Automatic Detection of Regions in Spinach Canopies Responding to Soil Moisture Deficit Using Combined Visible and Thermal Imagery

Thermal imaging has been used in the past for remote detection of regions of canopy showing symptoms of stress, including water deficit stress. Stress indices derived from thermal images have been used as an indicator of canopy water status, but these depend on the choice of reference surfaces and environmental conditions and can be confounded by variations in complex canopy structure. Therefore, in this work, instead of using stress indices, information from thermal and visible light imagery was combined along with machine learning techniques to identify regions of canopy showing a response to soil water deficit. Thermal and visible light images of a spinach canopy with different levels of soil moisture were captured. Statistical measurements from these images were extracted and used to classify between canopies growing in well-watered soil or under soil moisture deficit using Support Vector Machines (SVM) and Gaussian Processes Classifier (GPC) and a combination of both the classifiers. The classification results show a high correlation with soil moisture. We demonstrate that regions of a spinach crop responding to soil water deficit can be identified by using machine learning techniques with a high accuracy of 97%. This method could, in principle, be applied to any crop at a range of scales.

Local Edge Distributions for Detection of Salient Structure Textures and Objects

Automatic detection of regions of salient texture and objects is useful for analysis of remotely sensed imagery, such as for land cover classification, object detection, and change detection. Intuitively, the local edges on an image indicate spectral discontinuity and the existence of structure texture or objects. This letter explores a simple method for measuring the saliency of texture and objects based on the edge density and spatial evenness of the edge distribution in the local window of each pixel. This method generates a saliency map by computing the saliency index of each pixel. By segmenting the saliency map, the salient structure texture regions and the locations of objects can be extracted. The algorithm requires only the window size as the input parameter and is relatively simple to implement. Experiments using high-resolution images show its effectiveness and accuracy in the detection of salient structure texture regions, such as crops and residential areas, and man-made objects, such as airplanes, cars, etc.

An improvement of face detection algorithm for color photos

The problem of automatic detection of regions of interest (ROI) on color photos is considered. The efficiency of Viola-Jones face detector implemented in Intel OpenCV library is analyzed. The image size dependence of the algorithm characteristics is studied. It is shown that there are 2 significant drawbacks of this face detection algorithm as applied to the task mentioned. Some modifications are made for elimination of these drawbacks. To reduce false positives it is proposed to modify the algorithm with color segmentation and human skin tones analysis. To reduce the processing time the algorithm of downsizing preprocessing is proposed.

Evaluation of token-based acoustic measures for classification of irregular phonation in normal speech

Quantification of the acoustic characteristics of irregular phonation provides a foundation for automatic detection of regions of irregular phonation in continuous speech. Recent results for automatic classification of regions of phonation as either regular or irregular demonstrate classification rates greater than 90% (false positive <10%) [K. Surana, M.Eng. thesis, MIT, Cambridge, MA 2006]. Similar acoustic cues may be useful in separating subtypes of irregular phonation. Two types of irregular phonation are examined: (1) regions characterized by reduced airflow, assumed to correspond to tightly adducted vocal folds with brief regions of separation, and (2) regions characterized by increased airflow, assumed to correspond to a spread or spreading vocal-fold configuration [J. Slifka, J.Voice (in press)]. Reduced-airflow tokens are extracted, using airflow, audio, and electroglottography signals, from utterance-medial locations, and increased-airflow tokens are all utterance final (20 tokens/speaker, 4 speakers). Surana’s cue set was evaluated for the ability to separate these subtypes of irregular phonation. Preliminary results indicate that an energy difference measure (between tokens smoothed at 6 vs 16 ms) and normalized rms amplitude yield statistically separable populations. Limitations on the use of these cues are discussed including intra- and interspeaker variation and the effect of token duration. [Work supported by NIH DC02978.]

Automatic Region Detection of Maxillary Sinus from Human Face Radiographs in the Assessment of Recovery in Chronic Sinusitis

AbstractAutomatic region detection of the maxillary sinus from human face X-ray pictures is studied and presented.1 Firstly a new algorithm for transformation from X-ray images to bi-level images is introduced, which is based on the local properties of the images. Secondly, a region detection algorithm is used to determine global shape parameters of the maxillary sinus such as area, perimeter length, and complexity.It is shown that the regions of the maxillary sinus detected by the new algorithm coincide well with clinical diagnosis. It is also shown that the cumulative distribution function calculated from the density histogram of detected regions is a useful parameter for assessing the stage of recovery in chronic sinusitis.

Automatic Region Detection of Maxillary Sinus from Human Face X-ray Images

Automatic Region Detection of Maxillary Sinus from Human Face X-ray Images