We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems. Specifically, we distinguish between images which lead multiple annotators to segment different foreground objects (ambiguous) versus minor inter-annotator differences of the same object. Taking images from eight widely used datasets, we crowdsource labeling the images as "ambiguous" or "not ambiguous" to segment in order to construct a new dataset we call STATIC. Using STATIC, we develop a system that automatically predicts which images are ambiguous. Experiments demonstrate the advantage of our prediction system over existing saliency-based methods on images from vision benchmarks and images taken by blind people who are trying to recognize objects in their environment. Finally, we introduce a crowdsourcing system to achieve cost savings for collecting the diversity of all valid "ground truth" foreground object segmentations by collecting extra segmentations only when ambiguity is expected. Experiments show our system eliminates up to 47 of human effort compared to existing crowdsourcing methods with no loss in capturing the diversity of ground truths.
Numerous systems have been proposed to assist blind people to take a high quality picture of an object with a mobile phone camera @cite_27 @cite_45 @cite_20 @cite_16 @cite_28 . Unfortunately, such systems assume a user can localize the desired object and only help the user to improve the image focus @cite_27 , lighting @cite_45 , or composition @cite_20 @cite_16 @cite_28 . Unlike prior work, we do not assume a user can localize the object of interest. Rather, we propose a method that can be employed to automatically alert a blind user whether an image shows a single, unambiguous object. We demonstrate the predictive advantage of our system for this task over relying on saliency-based methods @cite_2 @cite_8 .
@cite_8 @cite_28 @cite_27 @cite_45 @cite_2 @cite_16 @cite_20
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Computer vision and human-powered services can provide blind people access to visual information in the world around them, but their efficacy is dependent on high-quality photo inputs. Blind people often have difficulty capturing the information necessary for these applications to work because they cannot see what they are taking a picture of. In this paper, we present Scan Search, a mobile application that offers a new way for blind people to take high-quality photos to support recognition tasks. To support realtime scanning of objects, we developed a key frame extraction algorithm that automatically retrieves high-quality frames from continuous camera video stream of mobile phones. Those key frames are streamed to a cloud-based recognition engine that identifies the most significant object inside the picture. This way, blind users can scan for objects of interest and hear potential results in real time. We also present a study exploring the tradeoffs in how many photos are sent, and conduct a user study with 8 blind participants that compares Scan Search with a standard photo-snapping interface. Our results show that Scan Search allows users to capture objects of interest more efficiently and is preferred by users to the standard interface. In this paper, we present a real-time obstacle detection system for the mobility improvement for the visually impaired using a handheld Smartphone. Though there are many existing assistants for the visually impaired, there is not a single one that is low cost, ultra-portable, non-intrusive and able to detect the low-height objects on the floor. This paper proposes a system to detect any objects attached to the floor regardless of their height. Unlike some existing systems where only histogram or edge information is used, the proposed systemcombines both cues and overcomes some limitations of existing systems. The obstacles on the floor in front of the user can be reliably detected in real time using the proposed system implemented on a Smartphone. The proposed system has been tested in different types of floor conditions and a field trial on five blind participants has been conducted. The experimental results demonstrate its reliability in comparison to existing systems. Camera cellphones have become ubiquitous, thus opening a plethora of opportunities for mobile vision applications. For instance, they can enable users to access reviews or price comparisons for a product from a picture of its barcode while still in the store. Barcode reading needs to be robust to challenging conditions such as blur, noise, low resolution, or low-quality camera lenses, all of which are extremely common. Surprisingly, even state-of-the-art barcode reading algorithms fail when some of these factors come into play. One reason resides in the early commitment strategy that virtually all existing algorithms adopt: The image is first binarized and then only the binary data are processed. We propose a new approach to barcode decoding that bypasses binarization. Our technique relies on deformable templates and exploits all of the gray-level information of each pixel. Due to our parameterization of these templates, we can efficiently perform maximum likelihood estimation independently on each digit and enforce spatial coherence in a subsequent step. We show by way of experiments on challenging UPC-A barcode images from five different databases that our approach outperforms competing algorithms. Implemented on a Nokia N95 phone, our algorithm can localize and decode a barcode on a VGA image (640 × 480, JPEG compressed) in an average time of 400-500 ms. A novel dataset for benchmarking image-based localization is presented. With increasing research interests in visual place recognition and localization, several datasets have been published in the past few years. One of the evident limitations of existing datasets is that precise ground truth camera poses of query images are not available in a meaningful 3D metric system. This is in part due to the underlying 3D models of these datasets are reconstructed from Structure from Motion methods. So far little attention has been paid to metric evaluations of localization accuracy. In this paper we address the problem of whether state-of-the-art visual localization techniques can be applied to tasks with demanding accuracy requirements. We acquired training data for a large indoor environment with cameras and a LiDAR scanner. In addition, we collected over 2000 query images with cell phone cameras. Using LiDAR point clouds as a reference, we employed a semi-automatic approach to estimate the 6 degrees of freedom camera poses precisely in the world coordinate system. The proposed dataset enables us to quantitatively assess the performance of various algorithms using a fair and intuitive metric.