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We introduce a light-weight automatic method to quickly capture and recover 2.5D multi-room indoor environments scaled to real-world metric dimensions. To minimize the user effort required, we capture and analyze a single omni-directional image per room using widely available mobile devices. Through a simple tracking of the user movements between rooms, we iterate the process to map and reconstruct...
The rapid growth of different types of images has posed a great challenge for scientific fraternity across the world. For easy access to large number of images, efficient indexing and retrieval is required. The field of Content-Based Image Retrieval (CBIR) attempts to solve this problem. This paper proposes a combination of local and global features for CBIR. Local features are extracted through Scale...
The goal of this minitrack is to offer a venue for research that focuses on digital innovation, broadly defined. This includes research into unique and specific effects of digital technologies on different forms of organizational innovation. In particular, new forms of service offerings, products, or ways of organizing processes that did not exist before the availability of large scale digitalization.
In this paper, a novel logo recognition algorithm based on a set of invariant features, which are calculated by using Radon transform and complex moments is proposed. This set of features is invariant to Rotation, Scaling, and Translation (RST) and it is also robust to additive noise. Radon transform is powerful tool for rotation, scaling, and translation properties which make it useful for our purpose...
Conjugate Symmetric Sequency-Ordered Complex Hadamard Transform (CS-SCHT) is a new version of Sequency-Ordered Complex Hadamard Transform. CS-SCHT is dyadic shift invariant and the spectrum has the conjugate symmetry property for real signals. This transform is very suitable to derive new shape descriptor because of its excellent properties. In this paper, CS-SCHT based shape descriptor (CS-SCHD)...
Bone edema is a nonspecific and reactive condition of bone which is easily detectable with PD weighted MRI. In this study we decomposed segmented PD weighted MR images of humeral head, based on finite discrete shearlet transform (FDST) which provides optimal multiscale and multidirectional representation of 2D signals. Afterwards shape features were extracted from coefficients of FDST based on Pyramid...
Hough Transform (HT) is commonly used to solve the line extraction problem. Although images are discretized at the onset, the Hough domain is continuous and in practice it has to be partitioned into cells. It has been suggested that the optimality of the size (resolution) of those cells would depend on the amount noise in the image. In this paper, we study the effect of discretization on the success...
We introduce a descriptor for shape feature extraction and matching using keypoints that are extracted from both the foreground and the background of binary images. First, distance transform (DT) is applied on the image after contour detection. Then, connected components (CCs) of pixels having the same intensity are extracted. Keypoints correspond to centers of mass of CCs. A keypoint filtering mechanism...
This paper proposes a novel approach to comparing cell colony images taken at different times on a Petri dish. The objective is to provide an assistive tool for microbiologists to quantify the loss of cell colonies on two Petri dishes, by mapping cell colonies between a pair of images. This problem is highly non-trivial, as the shape, size and position of the corresponding colonies vary randomly....
Recovering the motion of a non-rigid body from a set of monocular images permits the analysis of dynamic scenes in uncontrolled environments. However, the extension of factorisation algorithms for rigid structure from motion to the low-rank non- rigid case has proved challenging. This stems from the comparatively hard problem of finding a linear ``corrective transform'' which recovers the projection...
Road lane detection is a key problem in advanced driver-assistance systems (ADAS). For solving this problem, vision-based detection methods are widely used and are generally focused on edge information. However, only using edge information leads to miss detection and error detection in various road conditions. In this paper, we propose a neighbor-based image conversion method, called extremal-region...
Safe landing of small unmanned aerial vehicles (UAVs) is not an easy task due to complex external aiding devices. Likewise external piloting also involves risks of false landings. Hu's moment based vision algorithms that aids automatic landing of UAVs prove to be satisfying regarding speed but lack accuracy and efficiency. This paper proposes Randomized Hough transform (RHT) based vision algorithm...
For detection of oral potential malignant disorder (OPMD), devices such as VELscope, are potentially used in the oral cavity examination. The device area usually appears in the acquired images. A preprocessing stage, which consists of removing the device area, is required before the analysis of the images. In this paper we propose a method based on the Circular Hough transform (CHT) to detect auto...
In recent years, the medical image retrieval play an important role in the field of medical diagnosis. The primary goal is to retrieve accurate matching images from the database. In order to achieve this goal, quite a few methods were used in the past years and some of them can get realistic results. However, after some deep and further experiments, we found that some classic feature descriptors or...
It is important to distinguish overlapped cell for tracking and segmentation biological cells from images. In this research, a novel or comprehensive method is provided with morphological features of cell and minimum distance for overlapped cells separation (OCS). it’s necessary to say that this algorithm is not based on type and number of Overlapped cells. In this presented method based on a distance...
We propose a robust real-time person detection system, which aims to serve as solid foundation for developing solutions at an elevated level of reliability. Our belief is that clever handling of input data correlated with efficacious training algorithms are key for obtaining top performance. We introduce a comprehensive training method based on random sampling that compiles optimal classifiers with...
We consider the problem of comparing deformable 3D objects represented by graphs, i.e., Triangular tessellations. We propose a new algorithm to measure the distance between triangular tessellations using a new decomposition of triangular tessellations into triangle-Stars. The proposed algorithm assures a minimum number of disjoint triangle-Stars, offers a better measure by covering a larger neighborhood...
Detecting primitive geometric shapes, such as cylinders, planes and spheres, in 3D point clouds is an important building block for many high-level vision tasks. One approach for this detection is the Hough Transform, where features vote for parameters that explain them. However, as the voting space grows exponentially with the number of parameters, a full voting scheme quickly becomes impractical...
The dense matching of 3-D meshes is an important research topic in the field of computer vision. In this paper, we present a layered matching pipeline based on the mixed corresponding grid dense matching algorithm. Firstly, this algorithm find an intrinsic map between two non-isometric, genus zero surfaces. Secondly, we use the nature of the bottom of the measure preserving distance to released the...
In this paper we present an automatic volumetric liver localization method as an approach for liver segmentation. In the proposed method the aim is to localise a mean shape model of the liver in the target CT scan. The framework consists of three main steps: shape model construction, low level processing and shape model registration. We evaluated our method on the MICCAI 2007 liver segmentation challenge...
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