Stroke-Based Semi-automatic Region of Interest Detection Algorithm for In-Situ Painting Recognition

2011 ◽  
pp. 167-176 ◽  
Author(s):  
Youngkyoon Jang ◽  
Woontack Woo
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Interest Detection

scholarly journals Eye Tracking Interaction on Unmodified Mobile VR Headsets Using the Selfie Camera

2021 ◽  
Vol 18 (3) ◽  
pp. 1-20
Author(s):  
Panagiotis Drakopoulos ◽  
George-alex Koulieris ◽  
Katerina Mania
Keyword(s):  
Eye Tracking ◽  
Mixed Reality ◽  
Geometric Model ◽  
Region Of Interest ◽  
Search Space ◽  
Detection Algorithm ◽  
Head Tracking ◽  
Low Contrast ◽  
Image Enhancements ◽  
Interest Detection

Input methods for interaction in smartphone-based virtual and mixed reality (VR/MR) are currently based on uncomfortable head tracking controlling a pointer on the screen. User fixations are a fast and natural input method for VR/MR interaction. Previously, eye tracking in mobile VR suffered from low accuracy, long processing time, and the need for hardware add-ons such as anti-reflective lens coating and infrared emitters. We present an innovative mobile VR eye tracking methodology utilizing only the eye images from the front-facing (selfie) camera through the headset’s lens, without any modifications. Our system first enhances the low-contrast, poorly lit eye images by applying a pipeline of customised low-level image enhancements suppressing obtrusive lens reflections. We then propose an iris region-of-interest detection algorithm that is run only once. This increases the iris tracking speed by reducing the iris search space in mobile devices. We iteratively fit a customised geometric model to the iris to refine its coordinates. We display a thin bezel of light at the top edge of the screen for constant illumination. A confidence metric calculates the probability of successful iris detection. Calibration and linear gaze mapping between the estimated iris centroid and physical pixels on the screen results in low latency, real-time iris tracking. A formal study confirmed that our system’s accuracy is similar to eye trackers in commercial VR headsets in the central part of the headset’s field-of-view. In a VR game, gaze-driven user completion time was as fast as with head-tracked interaction, without the need for consecutive head motions. In a VR panorama viewer, users could successfully switch between panoramas using gaze.

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

Hierarchical region-of-interest detection

2006 ◽  
Vol 45 (7) ◽  
pp. 077201 ◽  
Author(s):  
Huibao Lin
Keyword(s):  
Region Of Interest ◽  
Interest Detection

Heterogeneity between proximal and distal aspects of occlusive thrombi on pretreatment imaging in acute ischemic stroke

2021 ◽  
pp. 197140092110497
Author(s):  
Tetsuya Hashimoto ◽  
Takenobu Kunieda ◽  
Tristan Honda ◽  
Fabien Scalzo ◽  
Latisha K Sharma ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Region Of Interest ◽  
Hounsfield Unit ◽  
Distal Segment ◽  
Large Vessel Occlusion ◽  
Anterior Circulation ◽  
Vessel Occlusion ◽  
The Difference

Background The potential heterogeneity in occlusive thrombi caused by in situ propagation by secondary thrombosis after embolic occlusion could obscure the characteristics of original thrombi, preventing the clarification of a specific thrombus signature for the etiology of ischemic stroke. We aimed to investigate the heterogeneity of occlusive thrombi by pretreatment imaging. Methods Among consecutive stroke patients with acute embolic anterior circulation large vessel occlusion treated with thrombectomy, we retrospectively reviewed 104 patients with visible occlusive thrombi on pretreatment non-contrast computed tomography admitted from January 2015 to December 2018. A region of interest was set on the whole thrombus on non-contrast computed tomography under the guidance of computed tomography angiography. The region of interest was divided equally into the proximal and distal segments and the difference in Hounsfield unit densities between the two segments was calculated. Results Hounsfield unit density in the proximal segment was higher than that in the distal segment (mean difference 4.45; p < 0.001), regardless of stroke subtypes. On multivariate analysis, thrombus length was positively correlated (β = 0.25; p < 0.001) and time from last-known-well to imaging was inversely correlated (β = −0.0041; p = 0.002) with the difference in Hounsfield unit densities between the proximal and distal segments. Conclusions The difference in density between the proximal and distal segments increased as thrombi became longer and decreased as thrombi became older after embolic occlusion. This time/length-dependent thrombus heterogeneity between the two segments is suggestive of secondary thrombosis initially occurring on the proximal side of the occlusion.

scholarly journals Recognition of Student Classroom Behaviors Based on Moving Target Detection

2021 ◽  
Vol 38 (1) ◽  
pp. 215-220
Author(s):  
Bin Wu ◽  
Chunmei Wang ◽  
Wei Huang ◽  
Da Huang ◽  
Hang Peng
Keyword(s):  
Target Detection ◽  
Classroom Environment ◽  
Skin Color ◽  
Student Behavior ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Moving Target ◽  
Classroom Behaviors ◽  
Moving Target Detection

Classroom teaching, as the basic form of teaching, provides students with an important channel to acquire information and skills. The academic performance of students can be evaluated and predicted objectively based on the data on their classroom behaviors. Considering the complexity of classroom environment, this paper firstly envisages a moving target detection algorithm for student behavior recognition in class. Based on region of interest (ROI) and face tracking, the authors proposed two algorithms to recognize the standing behavior of students in class. Moreover, a recognition algorithm was developed for hand raising in class based on skin color detection. Through experiments, the proposed algorithms were proved as effective in recognition of student classroom behaviors.

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