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For machines to interact with the physical world, they must understand the physical properties of objects and materials they encounter. We use fabrics as an example of a deformable material with a rich set of mechanical properties. A thin flexible fabric, when draped, tends to look different from a heavy stiff fabric. It also feels different when touched. Using a collection of 118 fabric samples,...
Effectively describing and recognizing leaf shapes under arbitrary deformations, particularly from a large database, remains an unsolved problem. In this research, we attempted a new strategy of describing shape by walking along a bunch of chords that pass through the shape to measure the regions trespassed. A novel chord bunch walks (CBW) descriptor is developed through the chord walking that effectively...
In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain streak accumulation. Our core ideas lie in our new rain image model and new deep learning architecture. We add a binary map that provides rain streak locations to an existing model, which comprises a rain streak layer and a background layer. We create a model consisting of a component...
3D face tracking using one monocular camera is an important topic, since it is useful in many domains such as: video surveillance system, human machine interaction, biometrics, etc. In this paper, we propose a new 3D face tracking which is robust to large head rotations. Underlying cascaded regression approach for 2D landmark detection, we build an extension in context of 3D pose tracking. To better...
Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points...
We present a new Cascaded Shape Regression (CSR) architecture, namely Dynamic Attention-Controlled CSR (DAC-CSR), for robust facial landmark detection on unconstrained faces. Our DAC-CSR divides facial landmark detection into three cascaded sub-tasks: face bounding box refinement, general CSR and attention-controlled CSR. The first two stages refine initial face bounding boxes and output intermediate...
Detection and segmentation of cells is an important step for classifying the cells as cancerous or non-cancerous. Pathologists use microscopic images for analysis and further diagnosis of cancer. These images contain the microscopic structure of tissues and are stained using some staining components to facilitate the process. Staining process varies due to different stain manufacturers, staining practices...
Self-Dual Attribute Profiles (SDAPs) have proven to be an effective method for extracting spatial features able to improve scene classification of remote sensing images with very high spatial resolution. An SDAP is a multilevel decomposition of an image obtained with a sequence of transformations performed by attribute filters over the Tree of Shapes (ToS). One of the main issues with this technique...
Oil spills and lookalikes (e.g. plant oil and oil emulsion) show the dark areas in SAR images, so it will bring some difficult in classifying the dark objects observed in full polarization SAR images into oil spills or lookalikes. In this paper, an approach is presented for distinguishing the dark areas in SAR images, which based on polarization features, geometric features and texture features fusion...
A method for classifying objects into categories and indexing is proposed to implement object recognition. The relational measurements such as the distance between two points, color comparison is encoded by the attributed relational graph (ARG) representation to provide one-to-one correspondence between models and object features. If the contour is traversed counterclockwise, a sequence can be formed...
This paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of...
We demonstrate an integrated strategy for identifying buildings in very high resolution satellite imagery of urban areas. Buildings are extracted using structural, contextual, and spectral information. We perform multi-resolution and spectral difference segmentation to obtain a proper object segmentation. First, we use One-Class support vector machine (SVM) in order to determine the man-made structures...
With increasing of the spatial resolution of satellite imaging sensors, object-based image analysis (OBIA) has been gaining prominence in remote sensing applications. However, scale selection in multi-scale segmentation and OBIA remains a challenge, which directly reduces efficiency of land cover mapping. In this study, we presented an object-based land cover mapping using adaptive scale segmentation...
Change detection techniques for remote sensing images are increasingly applied to many fields, such as disaster monitoring, vegetation coverage analysis and so on. How to improve the accuracy of detection has been a critical topic that confuse the researchers for a long time. In this paper, a method combining multiscale segmentation and fusion for high-resolution images is presented. The strategy...
Fall is one of the major health challenges facing the elderly adults, especially the adults with high fall risk factors. In this paper, we aim to build a video-based model to mitigate the consequences of fall of the elderly at two application scenarios: (1) predict the fall risk caused by unbalanced gait and (2) detect a fall event as soon as it happens. In the first stage, we use a common camera...
Vehicle detection and classification is an essential application in traffic surveillance system (TSS). However, recognizing moving vehicle at nighttime is more challenging because of either poorly (lack of street lights) or brightly illuminations and chaos traffic of motorbikes. Adding to this is various type of vehicles travels on the same road which falsifies the pairing results. So, this research...
Recently, object recognition techniques have been rapidly developed. Most of existing object recognition focused on recognizing several independent concepts. The relationship of objects is also an important problem, which shows in-depth semantic information of images. In this work, toward general visual relationship detection, we propose a method to integrate spatial distribution of object to facilitate...
Interest point detection is one of the key technologies in image processing and target recognition. This paper presents a new method for detecting interest points in digital images and computer vision problems based on complex network theory. We associate a directed and weighted complex network model to each image and then we propose three different algorithms to locate these key points based on three...
Traditional point tracking algorithms such as the KLT use local 2D information aggregation for feature detection and tracking, due to which their performance degrades at the object boundaries that separate multiple objects. Recently, CoMaL Features have been proposed that handle such a case. However, they proposed a simple tracking framework where the points are re-detected in each frame and matched...
Robotic graspable object recognition is a crucial ingredient in many exciting autonomous manipulation applications. However, identifying complex image features from limited data remains largely unsolved. In this paper, we leverage the advantages of two kinds of feature representation approaches, kernel descriptors and deep neural networks, to present a novel hierarchical feature learning framework...
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