Metaheuristic Algorithms Applied to Color Image Segmentation on HSV Space

In this research, we propose an unsupervised method for segmentation and edge extraction of color images on the HSV space. This approach is composed of two different phases in which are applied two metaheuristic algorithms, respectively the Firefly (FA) and the Artificial Bee Colony (ABC) algorithms. In the first phase, we performed a pixel-based segmentation on each color channel, applying the FA algorithm and the Gaussian Mixture Model. The FA algorithm automatically detects the number of clusters, given by histogram maxima of each single-band image. The detected maxima define the initial means for the parameter estimation of the GMM. Applying the Bayes’ rule, the posterior probabilities of the GMM can be used for assigning pixels to clusters. After processing each color channel, we recombined the segmented components in the final multichannel image. A further reduction in the resultant cluster colors is obtained using the inner product as a similarity index. In the second phase, once we have assigned all pixels to the corresponding classes of the HSV space, we carry out the second step with a region-based segmentation applied to the corresponding grayscale image. For this purpose, the bioinspired Artificial Bee Colony algorithm is performed for edge extraction.

Efficient Filtering for Edge Extraction under Perspective Effect

Though it is generally believed that edges should be extracted at different scales when using a linear filter, it is still difficult to determine the optimal scale for each filter. In this paper, we propose a novel approach called orientation and scale tuned difference of boxes (osDoB) to solve this problem. For certain computer vision applications, such as lane marking detection, the prior information about the concerned target can facilitate edge extraction in a top-down manner. Based on the perspective effect, we associate the scale of the edge in an image with the target size in the real world and assign orientation and scale parameters for filtering each pixel. Considering the fact that it is very time-consuming to naïvely perform filters with different orientations and scales, we further design an extended integration map technology to speed up filtering. Our method is validated on synthetic and real data. The experimental results show that assigning appropriate orientation and scale parameters for filters is effective and can be realized efficiently.

Dual-Mode Memristor Synaptic Circuit Design and Application in Image Processing

Memristor is a kind of synaptic element with nanometer size and continuously variable memristance. The bridge synaptic circuit constructed by the memristor has a simple structure and precise control. In practice, because of the non-linear characteristics of memristor, it is not easy to control synaptic circuit and errors in weights appear. Therefore, a novel memristor synaptic circuit is proposed in this paper, called the dual-mode memristor bridge synaptic neural network. The proposed method can make the weights more linear by controlling the input voltages and make the outputs more linear by using symmetrical positive and negative pulses. Therefore, the proposed synaptic circuit is easier to be controlled. In this paper, the numerical simulations are conducted and verify the feasibility. Furthermore, the simulation experiments are conducted for edge extraction of grayscale birds’ images in the airport for bird recognition applied for the bird repelling applications.

ENHANCING GEOMETRIC EDGE DETAILS IN MVS RECONSTRUCTION

Mesh models generated by multi view stereo (MVS) algorithms often fail to represent in an adequate manner the sharp, natural edge details of the scene. The harsh depth discontinuities of edge regions are eventually a challenging task for dense reconstruction, while vertex displacement during mesh refinement frequently leads to smoothed edges that do not coincide with the fine details of the scene. Meanwhile, 3D edges have been used for scene representation, particularly man-made built environments, which are dominated by regular planar and linear structures. Indeed, 3D edge detection and matching are commonly exploited either to constrain camera pose estimation, or to generate an abstract representation of the most salient parts of the scene, and even to support mesh reconstruction. In this work, we attempt to jointly use 3D edge extraction and MVS mesh generation to promote edge detail preservation in the final result. Salient 3D edges of the scene are reconstructed with state-of-the-art algorithms and integrated in the dense point cloud to be further used in order to support the mesh triangulation step. Experimental results on benchmark dataset sequences using metric and appearance-based measures are performed in order to evaluate our hypothesis.

A New Adaptive Method for the Extraction of Steel Design Structures from an Integrated Point Cloud

The continuous and intensive development of measurement technologies for reality modelling with appropriate data processing algorithms is currently being observed. The most popular methods include remote sensing techniques based on reflected-light digital cameras, and on active methods in which the device emits a beam. This research paper presents the process of data integration from terrestrial laser scanning (TLS) and image data from an unmanned aerial vehicle (UAV) that was aimed at the spatial mapping of a complicated steel structure, and a new automatic structure extraction method. We proposed an innovative method to minimize the data size and automatically extract a set of points (in the form of structural elements) that is vital from the perspective of engineering and comparative analyses. The outcome of the research was a complete technology for the acquisition of precise information with regard to complex and high steel structures. The developed technology includes such elements as a data integration method, a redundant data elimination method, integrated photogrammetric data filtration and a new adaptive method of structure edge extraction. In order to extract significant geometric structures, a new automatic and adaptive algorithm for edge extraction from a random point cloud was developed and presented herein. The proposed algorithm was tested using real measurement data. The developed algorithm is able to realistically reduce the amount of redundant data and correctly extract stable edges representing the geometric structures of a studied object without losing important data and information. The new algorithm automatically self-adapts to the received data. It does not require any pre-setting or initial parameters. The detection threshold is also adaptively selected based on the acquired data.