442 research outputs found
Design of automatic vision-based inspection system for solder joint segmentation
Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions
Constrained Design of Deep Iris Networks
Despite the promise of recent deep neural networks in the iris recognition
setting, there are vital properties of the classic IrisCode which are almost
unable to be achieved with current deep iris networks: the compactness of model
and the small number of computing operations (FLOPs). This paper re-models the
iris network design process as a constrained optimization problem which takes
model size and computation into account as learning criteria. On one hand, this
allows us to fully automate the network design process to search for the best
iris network confined to the computation and model compactness constraints. On
the other hand, it allows us to investigate the optimality of the classic
IrisCode and recent iris networks. It also allows us to learn an optimal iris
network and demonstrate state-of-the-art performance with less computation and
memory requirements
A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection
State-of-the-art person re-identification systems that employ a triplet based
deep network suffer from a poor generalization capability. In this paper, we
propose a four stream Siamese deep convolutional neural network for person
redetection that jointly optimises verification and identification losses over
a four image input group. Specifically, the proposed method overcomes the
weakness of the typical triplet formulation by using groups of four images
featuring two matched (i.e. the same identity) and two mismatched images. This
allows us to jointly increase the interclass variations and reduce the
intra-class variations in the learned feature space. The proposed approach also
optimises over both the identification and verification losses, further
minimising intra-class variation and maximising inter-class variation,
improving overall performance. Extensive experiments on four challenging
datasets, VIPeR, CUHK01, CUHK03 and PRID2011, demonstrates that the proposed
approach achieves state-of-the-art performance.Comment: Published in WACV 201
Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories
With the explosion in the availability of spatio-temporal tracking data in
modern sports, there is an enormous opportunity to better analyse, learn and
predict important events in adversarial group environments. In this paper, we
propose a deep decision tree architecture for discriminative dictionary
learning from adversarial multi-agent trajectories. We first build up a
hierarchy for the tree structure by adding each layer and performing feature
weight based clustering in the forward pass. We then fine tune the player role
weights using back propagation. The hierarchical architecture ensures the
interpretability and the integrity of the group representation. The resulting
architecture is a decision tree, with leaf-nodes capturing a dictionary of
multi-agent group interactions. Due to the ample volume of data available, we
focus on soccer tracking data, although our approach can be used in any
adversarial multi-agent domain. We present applications of proposed method for
simulating soccer games as well as evaluating and quantifying team strategies.Comment: To appear in 4th International Workshop on Computer Vision in Sports
(CVsports) at CVPR 201
SAIVT-QUT@TRECVid 2012: Interactive surveillance event detection
In this paper, we propose an approach which attempts to solve the problem of surveillance event detection, assuming that we know the definition of the events. To facilitate the discussion, we first define two concepts. The event of interest refers to the event that the user requests the system to detect; and the background activities are any other events in the video corpus. This is an unsolved problem due to many factors as listed below: 1) Occlusions and clustering: The surveillance scenes which are of significant interest at locations such as airports, railway stations, shopping centers are often crowded, where occlusions and clustering of people are frequently encountered. This significantly affects the feature extraction step, and for instance, trajectories generated by object tracking algorithms are usually not robust under such a situation. 2) The requirement for real time detection: The system should process the video fast enough in both of the feature extraction and the detection step to facilitate real time operation. 3) Massive size of the training data set: Suppose there is an event that lasts for 1 minute in a video with a frame rate of 25fps, the number of frames for this events is 60X25 = 1500. If we want to have a training data set with many positive instances of the event, the video is likely to be very large in size (i.e. hundreds of thousands of frames or more). How to handle such a large data set is a problem frequently encountered in this application. 4) Difficulty in separating the event of interest from background activities: The events of interest often co-exist with a set of background activities. Temporal groundtruth typically very ambiguous, as it does not distinguish the event of interest from a wide range of co-existing background activities. However, it is not practical to annotate the locations of the events in large amounts of video data. This problem becomes more serious in the detection of multi-agent interactions, since the location of these events can often not be constrained to within a bounding box. 5) Challenges in determining the temporal boundaries of the events: An event can occur at any arbitrary time with an arbitrary duration. The temporal segmentation of events is difficult and ambiguous, and also affected by other factors such as occlusions
Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition
In this paper we address the problem of human action recognition from video
sequences. Inspired by the exemplary results obtained via automatic feature
learning and deep learning approaches in computer vision, we focus our
attention towards learning salient spatial features via a convolutional neural
network (CNN) and then map their temporal relationship with the aid of
Long-Short-Term-Memory (LSTM) networks. Our contribution in this paper is a
deep fusion framework that more effectively exploits spatial features from CNNs
with temporal features from LSTM models. We also extensively evaluate their
strengths and weaknesses. We find that by combining both the sets of features,
the fully connected features effectively act as an attention mechanism to
direct the LSTM to interesting parts of the convolutional feature sequence. The
significance of our fusion method is its simplicity and effectiveness compared
to other state-of-the-art methods. The evaluation results demonstrate that this
hierarchical multi stream fusion method has higher performance compared to
single stream mapping methods allowing it to achieve high accuracy
outperforming current state-of-the-art methods in three widely used databases:
UCF11, UCFSports, jHMDB.Comment: Published as a conference paper at WACV 201
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 201
Uncertainty in Real-Time Semantic Segmentation on Embedded Systems
Application for semantic segmentation models in areas such as autonomous
vehicles and human computer interaction require real-time predictive
capabilities. The challenges of addressing real-time application is amplified
by the need to operate on resource constrained hardware. Whilst development of
real-time methods for these platforms has increased, these models are unable to
sufficiently reason about uncertainty present. This paper addresses this by
combining deep feature extraction from pre-trained models with Bayesian
regression and moment propagation for uncertainty aware predictions. We
demonstrate how the proposed method can yield meaningful uncertainty on
embedded hardware in real-time whilst maintaining predictive performance.Comment: 6 pages, 3 figure
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