Fast and robust recognition and localization of 2-D objects

Gerdes, R.; Otterbach, R.; Kammüller, Reiner

doi:10.1007/s001380050018

Cited by 7 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although it identifies manmade objects, no distinctions are made between them. Their approach was similar to that of [15], which reported a vision system for industrial scenes using a polygonal approximation of the object silhouette. The authors then pro-posed a two-stage matching algorithm.…”

Section: A Literature Reviewmentioning

confidence: 92%

“…8) First and second invariant moment: These features are derived from the second-order normalized central moments of the objects and are invariant to translation, rotation, and scale [17]. The moments are obtained as (11) and the central moments are then (12) where (13) The normalized central moments, denoted by are then (14) where (15) Finally, the first and second invariant moments can be derived as (16) 172) Inter-Frame Feature Measures: Four inter-frame feature measures are employed. These are the mean value of the feature, variance of the feature, mean rate of change of the feature, and variance of the rate of change of the feature.…”

Section: ) Intra-frame Feature Measuresmentioning

confidence: 99%

See 1 more Smart Citation

A comparison of inter-frame feature measures for robust object classification in sector scan sonar image sequences

Ruiz

Lane

Chantler

1999

IEEE J. Oceanic Eng.

View full text Add to dashboard Cite

This paper presents an investigation of the robustness of an inter-frame feature measure classifier for underwater sector scan sonar image sequences. In the initial stages the images are of either divers or remotely operated vehicles (ROV's). The inter-frame feature measures are derived from sequences of sonar scans to characterize the behavior of the objects over time. The classifier has been shown to produce error rates of 0%-2% using real nonnoisy images. The investigation looks at the robustness of the classifier with increased noise conditions and changes in the filtering of the images. It also identifies a set of features that are less susceptible to increased noise conditions and changes in the image filters. These features are the mean variance, and the variance of the rate of change in time of the intra-frame feature measures area, perimeter, compactness, maximum dimension and the first and second invariant moments of the objects. It is shown how the performance of the classifier can be improved. Success rates of up to 100% were obtained for a classifier trained under normal noise conditions, signal-to-noise ratio (SNR) around 9.5 dB, and a noisy test sequence of SNR 7.6 dB.

show abstract

Section: A Literature Reviewmentioning

confidence: 92%

Section: ) Intra-frame Feature Measuresmentioning

confidence: 99%

A comparison of inter-frame feature measures for robust object classification in sector scan sonar image sequences

Ruiz

Lane

Chantler

1999

IEEE J. Oceanic Eng.

View full text Add to dashboard Cite

show abstract

“…A polygonal approximation of any shape results in a loss of information (for instance, the degree of ellipticity is not well suited for recognition of polygons (94)). In (96), another approach has been presented based on a contour-oriented 2D object where recognition shows robustness towards polygonal approximation inconsistency. The major drawback however, is its high time complexity.…”

Section: Statistical Approaches To Symbol Recognition 31 Common Methodsmentioning

confidence: 99%

Graphical Symbol Recognition

Santosh

Wendling

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

View full text Add to dashboard Cite

The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic steps as well as prominent concepts or techniques and research standpoints/directions that are associated with graphical symbol recognition. Keywords. Graphics processing • graphical symbol • recognition, retrieval and spotting • statistical approaches • structural and syntactic approaches • real-world problems. '... the recurring wish for methods capable of efficiently combining structural and statistical methods' and 'the very structural and spatial nature of the information we work with makes structural methods quite natural in the community.' The problem can further be extended to symbol spotting, but one can view this as a kind of retrieval (29-33), which is guided by user queries. Additionally, the recognition/retrieval process can be made with the help of local descriptors like scale invariant feature transform (SIFT) and with the use of techniques like bag-of-features so that either primitive or region extraction (segmentation) can be avoided. The question always remains the same, 'what approach does what (i.e., performance) in which context?'. As mentioned earlier, graphics recognition has an extremely rich state-of-the-art literature in symbol recognition and localization (6; 9; 34; 35). But the major state-of-the-art methods for symbol recognition,

show abstract

“…(a more extensive overview is given in [15]). Symbolic approaches are applied significantly less often [17]. However, at the practical level, the production of representative and balanced training corpora is a challenging task, especially in such cases when surface manifestations of noise in image vary significantly.…”

Section: Two-stage Approach To Digit Recognitionmentioning

confidence: 99%

Putting Humans Back in the Loop: A Study in Human-Machine Cooperative Learning

Gnjatović¹,

Maček²,

Adamović³

2020

ACTA POLYTECH HUNG

View full text Add to dashboard Cite

This paper introduces a novel approach to human-machine collaborative learning that allows for the chronically missing human learnability in the context of supervised machine learning. The basic tenet of this approach is the refinement of a human designed software model through the iterative learning loop. Each iteration of the loop consists of two phases: (i) automatic data-driven parameter adjustment, performed by means of stochastic greedy local search, and (ii) human-driven model adjustment based on insights gained in the previous phase. The proposed approach is demonstrated through a real-life study of automatic electricity meter reading in the presence of noise. Thus, a cognitively-inspired non-connectionist approach to digit detection and recognition is introduced, which is subject to refinement through the iterative process of human-machine cooperation. The prototype system is evaluated with respect to the recognition accuracy (with the highest digit recognition accuracy of 94%), and also discussed with respect to the storage requirements, generalizability, utilized contextual information, and efficiency.  -threshold for digit detection in the pre-attentive processing stage, cf. Eq. (16) *  -threshold for graph-based segmentation in the attentive proc. stage, Sec. 2.3.2 * 2  -threshold for digit recognition in the attentive processing stage, cf. Eq. (19)*

show abstract

Fast and robust recognition and localization of 2-D objects

Cited by 7 publications

References 14 publications

A comparison of inter-frame feature measures for robust object classification in sector scan sonar image sequences

A comparison of inter-frame feature measures for robust object classification in sector scan sonar image sequences

Graphical Symbol Recognition

Putting Humans Back in the Loop: A Study in Human-Machine Cooperative Learning

Contact Info

Product

Resources

About