1,721,084 research outputs found
A Multi-Neural Network Approach to Image Detection and Segmentation of Gas Meter Counter
No full textLearning Object Detection using Multiple Neural Netwoks
No full textMultiple neural network systems have become popular techniques for tackling complex tasks, often giving improved performance compared to a single network. In this study we propose an innovative detection algorithm in image analysis using a multiple neural network approach where many neural networks are jointly used to solve the object detection problem. We use a group of networks configured with different parameters and features, then combines them in order to obtain new networks. The topology of the set of neural networks is statically configured as a tree where the root node produces in output the detection map. This work represents a preliminary study through which we want to move from detection to segmentation and recognition of
objects of interest. We have compared our model with other detection algorithms using a standard dataset and the results are encouraging. The results highlight the advantages and problems that will guide the evolution of
the proposed model.Multiple neural network systems have become popular techniques for tackling complex tasks, often giving improved performance compared to a single network. In this study we propose an innovative detection algorithm in image analysis using a multiple neural network approach where many neural networks are jointly used to solve the object detection problem. We use a group of networks configured with different parameters and features, then combines them in order to obtain new networks. The topology of the set of neural networks is statically configured as a tree where the root node produces in output the detection map. This work represents a preliminary study through which we want to move from detection to segmentation and recognition of objects of interest. We have compared our model with other detection algorithms using a standard dataset and the results are encouraging. The results highlight the advantages and problems that will guide the evolution of the proposed model
Cross-pollination of knowledge for object detection in domain adaptation for industrial automation
No full textA local and iterative neural reconstruction algorithm for cone-beam data
No full textThis work presents a new neural algorithm designed for the reconstruction of tomographic images from Cone Beam data. The neural network does not need a training set but uses the line integral of a single x-ray as ground-truth. The algorithm is iterative and based on a set of neural networks that are working locally and sequentially. The proposed strategy was compared with the iterative ART algorithm and the well known filtered backprojection (FBP) method. The results show how the proposed algorithm is much more accurate even in the presence of noise and under conditions of lack of data
Assigning Automatic Regularization Parameters in Image Restoration
No full textThis work aims to define and experimentally evaluate an adaptive strategy based on neural learning to select an appropriate regularization parameter within a regularized restoration process.
The appropriate setting of the regularization parameter within the restoration process
is a difficult task attempting to
achieve an optimal balance between removing edge ringing effects and suppressing additive noise.
In this context,in an attempt to overcome the limitations of trial and error and curve fitting procedures we propose the construction of the regularization parameter function through a training concept using a Multilayer Perceptron neural network.
The proposed solution is conceived independent from a specific restoration algorithm and can be included within a general local restoration procedure.
The proposed algorithm was experimentally evaluated and compared using test images with different levels of degradation.
Results obtained proven the generalization capability of the method that can be applied successfully on heterogeneous images never seen during training
An online document clustering technique for short web contents
No full textDocument clustering techniques have been applied in several areas, with the web as one of the most recent and influential. Both general-purpose and text-oriented techniques exist and can be used to cluster a collection of documents in many ways. This work proposes a novel heuristic online document clustering model that can be specialized with a variety of text-oriented similarity measures. An experimental evaluation of the proposed model was conducted in the e-commerce domain. Performances were measured using a clustering-oriented metric based on F-Measure and compared with those obtained by other well-known approaches. The obtained results confirm the validity of the proposed method both for batch scenarios and online scenarios where document collections can grow over time
Digital privacy: Replacing pedestrians from Google Street View images
No full textGiven the lack of modern techniques to ensure the digital privacy of individuals, we want to pave the way for a new approach to make pedestrians in cityscape images anonymous. To address these concerns, we propose an automated method to replace any unknown pedestrian with another one which is extracted from a controlled and authorized dataset. The techniques used up to now to make people anonymous are based mainly on the blurring of people's faces, but even so it is possible to trace the identity of the subject starting from his clothing, personal items, hairstyle, the place and time where the photo was taken. The proposed method aims to make the pedestrians completely anonymous, and consists of four phases: firstly we identify the area where the pedestrian is located, we separate the pedestrian from the background, we select the most similar pedestrian from a controlled dataset and subsequently we substitute it. Our case study is Google Street View because it is one of the online services which suffers most from this kind of privacy issues. The experimental results show how this technique can overcome the problems of digital privacy with promising results
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