Inspection System And Method For Bond Detection And Validation Of Surface Mount Devices Using Sensor Fusion And Active Perception

A hybrid surface mount component inspection system which includes both vision and infrared inspection techniques to determine the presence of surface mount components on a printed wiring board, and the quality of solder joints of surface mount components on printed wiring boards by using data level sensor fusion to combine data from two infrared sensors to obtain emissivity independent thermal signatures of solder joints, and using feature level sensor fusion with active perception to assemble and process inspection information from any number of sensors to determine characteristic feature sets of different defect classes to classify solder defects.Georgia Tech Research Corporatio

Semi-Supervised Learning with Scarce Annotations

While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small. In this work, we consider the problem of SSL multi-class classification with very few labelled instances. We introduce two key ideas. The first is a simple but effective one: we leverage the power of transfer learning among different tasks and self-supervision to initialize a good representation of the data without making use of any label. The second idea is a new algorithm for SSL that can exploit well such a pre-trained representation. The algorithm works by alternating two phases, one fitting the labelled points and one fitting the unlabelled ones, with carefully-controlled information flow between them. The benefits are greatly reducing overfitting of the labelled data and avoiding issue with balancing labelled and unlabelled losses during training. We show empirically that this method can successfully train competitive models with as few as 10 labelled data points per class. More in general, we show that the idea of bootstrapping features using self-supervised learning always improves SSL on standard benchmarks. We show that our algorithm works increasingly well compared to other methods when refining from other tasks or datasets.Comment: Workshop on Deep Vision, CVPR 202

Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases

In this paper, we introduce a novel framework for automatic Semantic Video Annotation. As this framework detects possible events occurring in video clips, it forms the annotating base of video search engine. To achieve this purpose, the system has to able to operate on uncontrolled wide-domain videos. Thus, all layers have to be based on generic features. This framework aims to bridge the "semantic gap", which is the difference between the low-level visual features and the human's perception, by finding videos with similar visual events, then analyzing their free text annotation to find a common area then to decide the best description for this new video using commonsense knowledgebases. Experiments were performed on wide-domain video clips from the TRECVID 2005 BBC rush standard database. Results from these experiments show promising integrity between those two layers in order to find expressing annotations for the input video. These results were evaluated based on retrieval performance

Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call "fooling images" (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.Comment: To appear at CVPR 201