Optimal scanning for faster object detection

Butko, Nicholas J.; Movellan, Javier R.

doi:10.1109/cvpr.2009.5206540

Cited by 70 publications

(29 citation statements)

References 15 publications

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“…Multi-scale algorithms usually specify a fixed set of scales with predetermined parameters of the detection regions [17], [50]. Choosing the scale automatically has the advantage since objects have different sizes and the size of the context neighborhood is also different.…”

Section: Prior Work On Quantitative Ultrasound Analysismentioning

confidence: 99%

Automatic Detection and Measurement of Structures in Fetal Head Ultrasound Volumes Using Sequential Estimation and Integrated Detection Network (IDN)

Sofka

Zhang

Good

et al. 2014

IEEE Trans. Med. Imaging

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Abstract-Routine ultrasound exam in the second and third trimesters of pregnancy involves manually measuring fetal head and brain structures in 2-D scans. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement calipers on the structures of interest. The process is tedious, time consuming, and introduces user variability into the measurements. This paper proposes an automatic fetal head and brain (AFHB) system for automatically measuring anatomical structures from 3-D ultrasound volumes. The system searches the 3-D volume in a hierarchy of resolutions and by focusing on regions that are likely to be the measured anatomy. The output is a standardized visualization of the plane with correct orientation and centering as well as the biometric measurement of the anatomy. The system is based on a novel framework for detecting multiple structures in 3-D volumes. Since a joint model is difficult to obtain in most practical situations, the structures are detected in a sequence, one-by-one. The detection relies on Sequential Estimation techniques, frequently applied to visual tracking. The interdependence of structure poses and strong prior information embedded in our domain yields faster and more accurate results than detecting the objects individually. The posterior distribution of the structure pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple structures and hierarchical levels. The probabilistic model helps solve many challenges present in the ultrasound images of the fetus such as speckle noise, signal drop-out, shadows caused by bones, and appearance variations caused by the differences in the fetus gestational age. This is possible by discriminative learning on an extensive database of scans comprising more than two thousand volumes and more than thirteen thousand annotations. The average difference between ground truth and automatic measurements is below 2 mm with a running time of 6.9 s (GPU) or 14.7 s (CPU). The accuracy of the AFHB system is within inter-user variability and the running time is fast, which meets the requirements for clinical use.Index Terms-Fetal brain measurements, fetal head measurements, fetal utrasound, object detection, sequential sampling, three-dimensional (3-D) ultrasound.

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Section: Prior Work On Quantitative Ultrasound Analysismentioning

confidence: 99%

Automatic Detection and Measurement of Structures in Fetal Head Ultrasound Volumes Using Sequential Estimation and Integrated Detection Network (IDN)

Sofka

Zhang

Good

et al. 2014

IEEE Trans. Med. Imaging

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“…An object of interest is salient if it is rare or novel to the surroundings. A variety of applications can be benefited from saliency modeling, e.g., object detection [2][3], image segmentation [4] [5], image retargeting [6] [7], image/video compression [8] [9], visual tracking [10][11], gaze estimation [12], robot navigation [13], image/video quality assessment [14] [15], and advertising design [16].…”

Section: Introductionmentioning

confidence: 99%

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Mahdi

Schlesinger

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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Abstract-Various saliency models have been developed over the years. The performance of saliency models is typically evaluated based on databases of experimentally recorded adult eye fixations. Although studies on infant gaze patterns have attracted much attention recently, saliency based models have not been widely applied for prediction of infant gaze patterns. In this study, we conduct a comprehensive comparison study of eight state-ofthe-art saliency models on predictions of experimentally captured fixations from infants and adults. Seven evaluation metrics are used to evaluate and compare the performance of saliency models. The results demonstrate a consistent performance of saliency models predicting adult fixations over infant fixations in terms of overlap, center fitting, intersection, information loss of approximation, and spatial distance between the distributions of saliency map and fixation map. In saliency and baselines models performance ranking, the results show that GBVS and Itti models are among the top three contenders, infants and adults have bias toward the centers of images, and all models and the center baseline model outperformed the chance baseline model.

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“…Some work has been done on this topic, mostly inspired by ideas from biological vision and attention research (Butko and Movellan 2009;Vogel and Freitas 2008). One application to the problem of visual detection picks features with maximum value of information in a Hough-voting framework (Vijayanarasimhan and Kapoor 2010).…”

Section: Using Feedbackmentioning

confidence: 99%

Anytime Recognition of Objects and Scenes

Karayev

Fritz

Darrell

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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Humans are capable of perceiving a scene at a glance, and obtain deeper understanding with additional time. Computer visual recognition should be similarly robust to varying computational budgets -a property we call Anytime recognition. We present a general method for learning dynamic policies to optimize Anytime performance in visual recognition. We approach this problem from the perspective of Markov Decision Processes, and use reinforcement learning techniques. Crucially, decisions are made at test time and depend on observed data and intermediate results. Our method is applicable to a wide variety of existing detectors and classifiers, as it learns from execution traces and requires no special knowledge of their implementation.We first formulate a dynamic, closed-loop policy that infers the contents of the image in order to decide which single-class detector to deploy next. We explain effective decisions for reward function definition and state-space featurization, and evaluate our method on the PASCAL VOC dataset with a novel costliness measure, computed as the area under an Average Precision (AP) vs. Time curve. In contrast to previous work, our method significantly diverges from predominant greedy strategies and learns to take actions with deferred values. If execution is stopped when only half the detectors have been run, our method obtains 66% better mean AP than a random ordering, and 14% better performance than an intelligent baseline.The detection actions are costly relative to the inference performed in executing our policy. Next, we apply our approach to a setting with less costly actions: feature selection for linear classification. We explain strategies for dealing with unobserved feature values that are necessary to effectively classify from any state in the sequential process. We show the applicability of this system to a challenging synthetic problem and to benchmark problems in scene and object recognition. On suitable datasets, we can additionally incorporate a semantic back-off strategy that gives maximally specific predictions for a desired level of accuracy. Our method delivers best results on the costliness measure, and provides a new view on the time course of human visual perception. 2Traditional visual recognition obtains significant advantages from the use of many features in classification. Recently, however, a single feature learned with multi-layer convolutional networks (CNNs) has outperformed all other approaches on the main recognition datasets. We propose Anytime-motivated methods for speeding up CNN-based detection approaches while maintaining their high accuracy: (1) a dynamic region selection method using novel quick-to-compute features; and (2) the Cascade CNN, which adds a reject option between expensive convolutional layers and allows the network to terminate some computation early. On the PASCAL VOC dataset, we achieve an 8x speed-up while losing no more than 10% of the top detection performance.Lastly, we address the problem of image style recognition, which has receiv...

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Optimal scanning for faster object detection

Cited by 70 publications

References 15 publications

Automatic Detection and Measurement of Structures in Fetal Head Ultrasound Volumes Using Sequential Estimation and Integrated Detection Network (IDN)

Automatic Detection and Measurement of Structures in Fetal Head Ultrasound Volumes Using Sequential Estimation and Integrated Detection Network (IDN)

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Anytime Recognition of Objects and Scenes

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