19 research outputs found
Optimized Block-based Connected Components Labeling with Decision Trees
In this paper we define a new paradigm for 8-connection labeling, which employes a general approach to improve neighborhood exploration and minimizes the number of memory accesses. Firstly we exploit and extend the decision table formalism introducing OR-decision tables, in which multiple alternative actions are managed. An automatic procedure to synthesize the optimal decision tree from the decision table is used, providing the most effective conditions evaluation order. Secondly we propose a new scanning technique that moves on a 2x2 pixel grid over the image, which is optimized by the automatically generated decision tree.An extensive comparison with the state of art approaches is proposed, both on synthetic and real datasets. The synthetic dataset is composed of different sizes and densities random images, while the real datasets are an artistic image analysis dataset, a document analysis dataset for text detection and recognition, and finally a standard resolution dataset for picture segmentation tasks. The algorithm provides an impressive speedup over the state of the art algorithms
Parallel Image Processing Using a Pure Topological Framework
Image processing is a fundamental operation
in many real time applications, where lots of parallelism
can be extracted. Segmenting the image into different
connected components is the most known operations, but
there are many others like extracting the region adjacency
graph (RAG) of these regions, or searching for features
points, being invariant to rotations, scales, brilliant
changes, etc. Most of these algorithms part from the basis
of Tracing-type approaches or scan/raster methods. This
fact necessarily implies a data dependence between the
processing of one pixel and the previous one, which
prevents using a pure parallel approach. In terms of time
complexity, this means that linear order O(N) (N being the
number of pixels) cannot be cut down. In this paper, we
describe a novel approach based on the building of a pure
Topological framework, which allows to implement fully
parallel algorithms. Concerning topological analysis, a first
stage is computed in parallel for every pixel, thus
conveying the local neighboring conditions. Then, they are
extended in a second parallel stage to the necessary global
relations (e.g. to join all the pixels of a connected
component). This combinatorial optimization process can
be seen as the compression of the whole image to just one
pixel. Using this final representation, every region can be
related with the rest, which yields to pure topological
construction of other image operations. Besides, complex
data structures can be avoided: all the processing can be
done using matrixes (with the same indexation as the
original image) and element-wise operations. The time
complexity order of our topological approach for a m×n
pixel image is near O(log(m+n)), under the assumption that
a processing element exists for each pixel. Results for a
multicore processor show very good scalability until the
memory bandwidth bottleneck is reached, both for bigger
images and for much optimized implementations. The
inherent parallelism of our approach points to the
direction that even better results will be obtained in other
less classical computing architectures.1Ministerio de Economía y Competitividad (España) TEC2012-37868-C04-02AEI/FEDER (UE) MTM2016-81030-PVPPI of the University of Sevill
U-Capkidnets++-: A Novel Hybrid Capsule Networks with Optimized Deep Feed Forward Networks for an Effective Classification of Kidney Tumours Using CT Kidney Images
Chronic Kidney Diseases (CKD) has become one among the world wide health crisis and needs the associated efforts to prevent the complete organ damage. A considerable research effort has been put forward onto the effective seperation and classification of kidney tumors from the kidney CT Images. Emerging machine learning along with deep learning algorithms have waved the novel paths of tumor detections. But these methods are proved to be laborious and its success rate is purely depends on the previous experiences. To achieve the better classification and segmentation of tumors, this paper proposes the hybrid ensemble of visual capsule networks in U-NET deep learning architecture and w deep feed-forward extreme learning machines. The proposed framework incorporates the data-preprocessing powerful data augmentation, saliency tumor segmentation (STS) followed by the classification phase. Furthermore, classification levels are constructed based upon the feed forward extreme learning machines (FFELM) to enhance the effectiveness of the suggested model .The extensive experimentation has been conducted to evaluate the efficacy of the recommended structure and matched with the other prevailing hybrid deep learning model. Experimentation demonstrates that the suggested model has showed the superior predominance over the other models and exhibited DICE co-efficient of kidney tumors as high as 0.96 and accuracy of 97.5 %respectively
Level set-based topology optimization considering aesthetic preferences based on texture energy
For improving consumer satisfaction, a design process for individual production based on additive manufacturing technology is required to consider appearance as well as functionality. Although topology optimization is a powerful technology to design highly functional structure, it has difficulty considering aesthetic features. This paper presents a new topology optimization method considering aesthetic preferences with a manufacturing constraint by incorporating the image processing used for style transfer and object recognition. To consider aesthetic features, we introduce texture energy which evaluates the similarity between the input preference image and structure represented by the level set method. To identify the unmanufacturable regions disconnected from the main structure, the connected component labeling process based on the object recognition method is applied to the binary image of the level set function. A topology optimization problem of maximizing stiffness is formulated considering aesthetic preferences and imposing the structural connectivity constraint, where the objective function is defined as a combination of minimizing mean compliance and texture energy. A reaction diffusion equation is used to update the level set function, where the Lagrange multiplier of structural connectivity constraint is calculated to eliminate unmanufacturable disconnect regions. Numerical examples are provided to confirm the validity and utility of the proposed method
CylinderTag: An Accurate and Flexible Marker for Cylinder-Shape Objects Pose Estimation Based on Projective Invariants
High-precision pose estimation based on visual markers has been a thriving
research topic in the field of computer vision. However, the suitability of
traditional flat markers on curved objects is limited due to the diverse shapes
of curved surfaces, which hinders the development of high-precision pose
estimation for curved objects. Therefore, this paper proposes a novel visual
marker called CylinderTag, which is designed for developable curved surfaces
such as cylindrical surfaces. CylinderTag is a cyclic marker that can be firmly
attached to objects with a cylindrical shape. Leveraging the manifold
assumption, the cross-ratio in projective invariance is utilized for encoding
in the direction of zero curvature on the surface. Additionally, to facilitate
the usage of CylinderTag, we propose a heuristic search-based marker generator
and a high-performance recognizer as well. Moreover, an all-encompassing
evaluation of CylinderTag properties is conducted by means of extensive
experimentation, covering detection rate, detection speed, dictionary size,
localization jitter, and pose estimation accuracy. CylinderTag showcases
superior detection performance from varying view angles in comparison to
traditional visual markers, accompanied by higher localization accuracy.
Furthermore, CylinderTag boasts real-time detection capability and an extensive
marker dictionary, offering enhanced versatility and practicality in a wide
range of applications. Experimental results demonstrate that the CylinderTag is
a highly promising visual marker for use on cylindrical-like surfaces, thus
offering important guidance for future research on high-precision visual
localization of cylinder-shaped objects. The code is available at:
https://github.com/wsakobe/CylinderTag.Comment: 15 pages, 22 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl