Efficient Region Of Interest Research Articles

An efficient ROI detection algorithm for Bangla text extraction and recognition from natural scene images

This research work plays a significant role in finding information from the scene images to fulfill the demand of real life applications like detection of license plate, navigation of robot and helping the visually impaired persons. Here, a new algorithm has been proposed and applied on the scene images to extract Region of Interest (ROI). All the Bangla words are then separated from a sentence by analyzing and applying the Connected Component (CC) method along with bounding box technology. Another new algorithm has been proposed and applied to apart and bring-out Bangla characters from the Bangla words. This algorithm works by the method of vertical scanning of the images of Bangla words. Finally, the extracted characters are recognized by using the Support Vector Machine (SVM) as a classifier which works with Histogram of Oriented Gradient (HOG) features. There are 500 scene images with variations in colors, writing styles and orientations in our designed database. The proposed algorithm yields the accuracy 92.70% and 93.23% in extraction of ROI and character respectively. In the recognition of Bangla characters (digits, Basic characters, and joined characters), the average accuracy is 99.16%. The recognition accuracy of Bangla characters using Convolutional Neural Network (CNN) is also calculated and the obtained result is 83.52%.

Robust Lane Detection and Tracking for Real-Time Applications

An effective lane-detection algorithm is a fundamental component of an advanced driver assistant system, as it provides important information that supports driving safety. The challenges faced by the lane detection and tracking algorithm include the lack of clarity of lane markings, poor visibility due to bad weather, illumination and light reflection, shadows, and dense road-based instructions. In this paper, a robust and real-time vision-based lane detection algorithm with an efficient region of interest is proposed to reduce the high noise level and the calculation time. The proposed algorithm also processes a gradient cue and a color cue together and a line clustering with scan-line tests to verify the characteristics of the lane markings. It removes any false lane markings and tracks the real lane markings using the accumulated statistical data. The experiment results show that the proposed algorithm gives accurate results and fulfills the real-time operation requirement on embedded systems with low computing power.

An efficient ROI based copyright protection scheme for digital images with SVD and orthogonal polynomials transformation

In this paper, an efficient copyright protection scheme, based on Region of Interest (ROI), Singular Value Decomposition (SVD) and Orthogonal Polynomials Transformation (OPT) is proposed. Initially, the proposed copyright protection scheme separates the edge blocks with maximizing signal-to-noise ratio in orthogonal polynomials domain and arranges them in descending order. Next, the host image is transformed into OPT domain and DC coefficients are selected to form the OPT-DC map and ROIs are selected in the map with secret image. Then, Singular Value Decomposition (SVD) is applied on the selected ROIs to construct master share and finally ownership share is generated from master share with secret image. Extensive experiment and analysis have been conducted to demonstrate the robustness of the proposed scheme against various image-manipulation attacks.

An Automatic Bleeding Frame and Region Detection Scheme for Wireless Capsule Endoscopy Videos Based on Interplane Intensity Variation Profile in Normalized RGB Color Space.

Wireless capsule endoscopy (WCE) is an effective video technology to diagnose gastrointestinal (GI) disease, such as bleeding. In order to avoid conventional tedious and risky manual review process of long duration WCE videos, automatic bleeding detection schemes are getting importance. In this paper, to investigate bleeding, the analysis of WCE images is carried out in normalized RGB color space as human perception of bleeding is associated with different shades of red. In the proposed method, at first, from the WCE image frame, an efficient region of interest (ROI) is extracted based on interplane intensity variation profile in normalized RGB space. Next, from the extracted ROI, the variation in the normalized green plane is presented with the help of histogram. Features are extracted from the proposed normalized green plane histograms. For classification purpose, the K-nearest neighbors classifier is employed. Moreover, bleeding zones in a bleeding image are extracted utilizing some morphological operations. For performance evaluation, 2300 WCE images obtained from 30 publicly available WCE videos are used in a tenfold cross-validation scheme and the proposed method outperforms the reported four existing methods having an accuracy of 97.86%, a sensitivity of 95.20%, and a specificity of 98.32%.

Filtering Based ROI Coding Using Dynamic Range Compression and Updating Source Picture Filter

The region of interest (ROI) coding, which compresses background region more strongly than foreground region, is valuable technology in restricted communication environments such as limited bandwidth and/or storage capacity. This paper proposes a filtering scheme which reduces the amount of bits by filtering background region in order to support efficient ROI coding on ordinary devices without equipping ROI coding functionality. Specifically, dynamic range compression (DRC) filter and updating source picture (USP) filter are added to a low-pass filter, which has been used for conventional filtering scheme. The DRC filter compresses dynamic range of all pixels by down-scaling. The USP filter measures distance between current and previous picture by block basis, and then overwrites the block of the current picture when the distance is smaller than predetermined threshold. Experimental results show that the proposed filtering scheme reduces bit-rate by half compared to the conventional low-pass filter in the condition with the PSNR-Y of background region kept to 27 dB. Compared to uniform quality coding which does not incur the filters, there is the case that the proposed filtering scheme reduces bit-rate by 58 %. These results promise to cut down on cost of communication and storage.

Region of interest extraction based on multiscale visual saliency analysis for remote sensing images

Region of interest (ROI) extraction is an important component of remote sensing image processing. However, traditional ROI extraction methods are usually prior knowledge-based and depend on classification, segmentation, and a global searching solution, which are time-consuming and computationally complex. We propose a more efficient ROI extraction model for remote sensing images based on multiscale visual saliency analysis (MVS), implemented in the CIE L*a*b* color space, which is similar to visual perception of the human eye. We first extract the intensity, orientation, and color feature of the image using different methods: the visual attention mechanism is used to eliminate the intensity feature using a difference of Gaussian template; the integer wavelet transform is used to extract the orientation feature; and color information content analysis is used to obtain the color feature. Then, a new feature-competition method is proposed that addresses the different contributions of each feature map to calculate the weight of each feature image for combining them into the final saliency map. Qualitative and quantitative experimental results of the MVS model as compared with those of other models show that it is more effective and provides more accurate ROI extraction results with fewer holes inside the ROI.

ROI analysis for remote photoplethysmography on facial video.

As wide spreading of camera-equipped devices to the daily living environment, there are enormous opportunities to utilize the camera-based remote photoplethysmography (PPG) for daily physiological monitoring. In the camera-based remote PPG (rPPG) monitoring, the region of interest (ROI) is related to the signal quality and the computational load for the signal extraction processing. Designating the best ROI on the body while minimizing its size is essential for computationally efficient rPPG extraction. In this study, we densely analyzed the face region to find the computationally efficient ROI for facial rPPG extraction. We divided the face into seven regions and evaluated the quality of the signal of each region using the area ratio of high-SNR and high-correlation, and mean and standard deviation (SD) of SNR and correlation coefficient. The results show that a forehead and both cheeks especially have a potential to be a good candidates for computationally efficient ROI. On the other hand, the signal quality from a mouth and a chin was relatively low. A nasion and a nose have a limitation to be efficient ROI.

Region of Interest Detection Based on Visual Attention and Threshold Segmentation in High Spatial Resolution Remote Sensing Images

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the “strong points” and the “salient points”. A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti’s model.

Detection of regions of interest in a high-spatial-resolution remote sensing image based on an adaptive spatial subsampling visual attention model

Traditional prior-knowledge-based region of interest (ROI) detection methods for processing high-resolution remote sensing images generally use global searching, which largely leads to prohibitive computational complexity. As an attempt to solve this problem, in the present study, a faster, more efficient ROI detection algorithm based on an adaptive spatial subsampling visual attention model (ASS-VA) is proposed. In the ASS-VA model, a visual attention mechanism is used to avoid applying image segmentation and feature detection to the entire image. The adaptive spatial subsampling strategy is formulated to decrease the computational complexity of ROI detection. A discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. In addition, a region growing strategy is employed to obtain more accurate shape information of ROIs. Experimental results show that the time spent on detection using the new algorithm is only 2-4% of that expended in the traditional visual attention model and the detection results are visually more accurate.

Multiple Region-of-Interest Based H.264 Encoder with a Detection Architecture in Macroblock Level Pipelining

This paper proposes a region-of-interest (ROI) based H.264 encoder and the VLSI architecture of the ROI detection algorithm. In ROI based video coding system, pre-processing unit to detect ROI should only introduce low computational complexity overhead due to the low power requirement. The Macroblocks (MBs) in ROIs are detected sequentially in the same order of H.264 encoding to satisfy the MB level pipelining of ROI detector and H.264 encoder. ROI detection is performed in a novel estimation-and-verification process with an ROI contour template. Proposed architecture can be configured to detect either single ROI or multiple ROIs in each frame and the throughput of single detection mode is 5.5 times of multiple detection mode. 98.01% and 97.89% of MBs in ROIs can be detected in single and multiple detection modes respectively. Hardware cost of proposed architecture is only 4.68k gates. Detection speed is 753fps for CIF format video at the operation frequency of 200MHz in multiple detection mode with power consumption of 0.47mW. Compared with previous fast ROI detection algorithms for video coding application, the proposed architecture obtains more accurate and smaller ROI. Therefore, more efficient ROI based computation complexity and compression efficiency optimization can be implemented in H.264 encoder.

Real-Time Pedestrian Detection and Tracking at Nighttime for Driver-Assistance Systems

Pedestrian detection is one of the most important components in driver-assistance systems. In this paper, we propose a monocular vision system for real-time pedestrian detection and tracking during nighttime driving with a near-infrared (NIR) camera. Three modules (region-of-interest (ROI) generation, object classification, and tracking) are integrated in a cascade, and each utilizes complementary visual features to distinguish the objects from the cluttered background in the range of 20-80 m. Based on the common fact that the objects appear brighter than the nearby background in nighttime NIR images, efficient ROI generation is done based on the dual-threshold segmentation algorithm. As there is large intraclass variability in the pedestrian class, a tree-structured, two-stage detector is proposed to tackle the problem through training separate classifiers on disjoint subsets of different image sizes and arranging the classifiers based on Haar-like and histogram-of-oriented-gradients (HOG) features in a coarse-to-fine manner. To suppress the false alarms and fill the detection gaps, template-matching-based tracking is adopted, and multiframe validation is used to obtain the final results. Results from extensive tests on both urban and suburban videos indicate that the algorithm can produce a detection rate of more than 90% at the cost of about 10 false alarms/h and perform as fast as the frame rate (30 frames/s) on a Pentium IV 3.0-GHz personal computer, which also demonstrates that the proposed system is feasible for practical applications and enjoys the advantage of low implementation cost.