Download Advances in Pattern Recognition — ICAPR 2001: Second by H. Bunke, S. Günter, X. Jiang (auth.), Sameer Singh, Nabeel PDF

By H. Bunke, S. Günter, X. Jiang (auth.), Sameer Singh, Nabeel Murshed, Walter Kropatsch (eds.)

The paper is equipped as follows: In part 2, we describe the no- orientation-discontinuity interfering version in accordance with a Gaussian stochastic version in interpreting the homes of the interfering strokes. In part three, we describe the enhanced canny facet detector with an ed- orientation constraint to discover the sides and get better the vulnerable ones of the foreground phrases and characters; In part four, we illustrate, talk about and evaluation the experimental result of the proposed strategy, demonstrating that our set of rules considerably improves the segmentation caliber; part five concludes this paper. 2. The norm-orientation-discontinuity interfering stroke version determine 2 indicates 3 usual samples of unique photograph segments from the unique records and their significance of the detected edges respectively. The value of the gradient is switched over into the grey point worth. The darker the sting is, the bigger is the gradient value. it truly is noticeable that the topmost robust edges correspond to foreground edges. it's going to be famous that, whereas frequently, the foreground writing appears to be like darker than the history photograph, as proven in pattern photo determine 2(a), there are situations the place the foreground and historical past have comparable intensities as proven in determine 2(b), or worst nonetheless, the history is extra trendy than the foreground as in determine 2(c). So utilizing simply the depth worth isn't sufficient to distinguish the foreground from the heritage. (a) (b) (c) (d) (e) (f)

Show description

Read or Download Advances in Pattern Recognition — ICAPR 2001: Second International Conference Rio de Janeiro, Brazil, March 11–14, 2001 Proceedings PDF

Similar international conferences and symposiums books

Discrete Geometry for Computer Imagery: 6th International Workshop, DGCI'96 Lyon, France, November 13–15, 1996 Proceedings

This e-book constitutes the refereed court cases of the sixth foreign Workshop on Discrete Geometry for laptop Imagery, DGCI'96, held in Lyon, France, in November 1996. computing device imaging primarily is dependent upon discrete versions for coding, processing, popularity, illustration, and so forth. the quantity offers 24 revised complete papers chosen from forty-one submissions including three invited contributions and an educational paper, which bridges the distance among thought and perform.

Affective Computing and Intelligent Interaction: First International Conference, ACII 2005, Beijing, China, October 22-24, 2005. Proceedings

This quantity includes the complaints of the first foreign convention on A? ective Computing and clever interplay (ACII 2005) held in Beijing, China, on 22–24 October 2005. generally, the computer finish of human–machine interplay has been very passive, and definitely has had no technique of spotting or expressing a?

Formal Approaches to Software Testing: 4th International Workshop, FATES 2004, Linz, Austria, September 21, 2004, Revised Selected Papers

Trying out usually debts for greater than 50% of the mandatory e? ort in the course of procedure improvement. Thechallengeforresearchistoreducethesecostsbyprovidingnew equipment for the speci? cation and iteration of fine quality exams. event has proven that using formal equipment in checking out represents a crucial potential for making improvements to the trying out strategy.

Privacy, Security, and Trust in KDD: First ACM SIGKDD International Workshop, PinKDD 2007, San Jose, CA, USA, August 12, 2007, Revised Selected Papers

This e-book constitutes the completely refereed post-workshop lawsuits of the 1st overseas Workshop on privateness, defense, and belief in KDD, PinKDD 2007, held in San Jose, CA, united states, in August 2007 along side the thirteenth ACM SIGKDD overseas convention on wisdom Discovery and knowledge Mining, KDD 2007.

Additional info for Advances in Pattern Recognition — ICAPR 2001: Second International Conference Rio de Janeiro, Brazil, March 11–14, 2001 Proceedings

Example text

In general, the neural network part is used for its learning, while the fuzzy logic part is used for representing knowledge. Learning capability is fundamentally performed as necessary change such as incremental learning, back propagation method and delta rule based on error functions. EC can also tune NN and FI. However, evolution can be defined as resultant or accidental change, not necessary change, since the EC can not predict and estimate the effect of the change. To summarize, an intelligent system can quickly adapt to dynamic environment by NN and FI with the back propagation method or delta rule, and furthermore, the structure of intelligent system can globally evolve by EC according to the objective problems.

3 Synthesized Approach To realize higher intelligent system, a synthesized algorithm of various techniques is required. Figure 2 shows the synthesis of NN, FL and EC. Each technique plays the peculiar role for intelligent function. There are not complete techniques for realizing all features of intelligence. Therefore, we should integrate and combine some techniques to compensate the disadvantages of each technique. The main characteristics of NN are to classify or recognize patterns, and to adapt itself to dynamic environments by learning, but the mapping structure of NN is a black box and incomprehensible.

Hlavác ÒÓØ Ö Ð ÒÙÑ Ö׺ ÀÓÛ Ú Ö¸ Ø Ö × ×ÓÑ ÙÒ Ø ÓÒ Á ´Ù Ú µ Ò ¸ ÓÖ Û Ù Ò Ú Ö Ö Ðº Ï Ù×Ø × ÑÔÐ Ø Ø × Ö Ø ÔÓ ÒØ׺ Á Ø Ñ Ö ÖÓØ Ø × Ý Ö Ð Ò Ð «¸ Û Ó Ø Ò Â ´Ñ Òµ × ÑÔÐ Á ´Ù Ú ³µ ¸ Û Ö ³ ¾Æ «¸ Ò Å Ò Æ Ö Ø ÖÓÛ Ò Ø ÓÐÙÑÒ × Þ × Ó Ø Ñ Ö ×Ô Ø Ú Ðݺ × Ø ¬Ö×Ø ÖÑÓÒ Ó Á ´Ñ Òµ × ××ÙÑ ØÓ ÒÓÒÞ ÖÓ¸ Ø× Ô × × ¹ Ø Ý × Ø Ò Á ´Ù Ú µº Ï Ò Ð Ñ Ò Ø ÒÝ ÙÒ ÒÓÛÒ × Ø Ý × Ø Ò Ø ÒØ ÖÔÓÐ Ø ÓÒ Ó Á ´Ñ Òµ Ò Ö ¹× ÑÔÐ Ò Ø ×Ó Ø Ø Ø ¬Ö×Ø ÖÑÓÒ Ó Ø Ø Ö ×ÙÐØ Ò Ö ÔÖ × ÒØ Ø Ú Ñ Ó × Ø ÕÙ Ú Ð Ò Ð ×× Á £ ´Ñ Òµ ÕÙ Ð× Þ ÖÓº Ä Ø Ù× × ÓÛ ÓÛ ØÓ ÓÑÔÙØ Á £ Ò ½ º ¬Ò Ø ÓÒ ½ ´Ë صº Ä Ø ÙÒ Ø ÓÒ ´Òµ Ê Ô Ö Ó ÓÒ Ø ÒØ ÖÚ Ð ¼ Ƹ ºº ´Òµ ´Ò · Æ µ ´½µ Å ÔÔ Ò ¨³ Ê Ê ½ ¨³ ´Òµ ´Òµ ¾ Ƴ ´¾µ Û Ö ´ µ ´Òµ ÒÓØ × Ø × Ö Ø ÓÙÖ Ö ÌÖ Ò× ÓÖÑ Ó ´Òµ¸ Ò × ÐÐ Ø × Ø Ó ´Òµ Ý Ô × ³º ¬Ò Ø ÓÒ ¾ ´Ë Ø ÕÙ Ú Ð Ò Ð ××µº Ä Ø ÙÒ Ø ÓÒ ´Òµ Ê Ô ÖÓ ÓÒ ÒØ ÖÚ Ð ¼ Æ º Ì Ò¸ Ø × Ø Ë ´Òµ ´Òµ ´Òµ ³¾Ê ¨³ ´Òµ ´¿µ ÐÐ Ø × Ø ÕÙ Ú Ð Ò Ð ×× Ò Ö Ø Ý ´Òµº ¬Ò Ø ÓÒ ¿ ´Ê ÔÖ × ÒØ Ø Ú ÙÒ Ø ÓÒ Ó × Ø ÕÙ Ú Ð Ò Ð ××µº Ä Ø ÙÒ Ø ÓÒ ´Òµ Ê Ô Ö Ó ÓÒ ÒØ ÖÚ Ð ¼ Æ º ÙÒ Ø ÓÒ Ö´Òµ × Ö ÔÖ × ÒØ Ø Ú ÙÒ Ø ÓÒ Ó Ë ´Òµ « Ø Ò Ö Ø × Ë ´Òµ ¸ º º × Ë ´Òµ ´Òµ ´Òµ ³¾Ê ¨³ Ö´Òµ ÁÒ ÓØ Ö ÛÓÖ ×¸ Ö´Òµ × Ö ÔÖ × ÒØ Ø Ú ÙÒ Ø ÓÒ Ó Ø× Ñ Ñ Ö¸ º º Ö´Òµ ¾ Ë ´Òµ º Ò ÕÙ Ú Ð Ò ´ µ Ð ×× « Ø × ¬Ò Ø ÓÒ ´Ë Ø ÒÚ Ö ÒØ Ö ÔÖ × ÒØ Ø ÓÒµº Ä Ø ÙÒ Ø ÓÒ ´Òµ Ô Ö Ó ÓÒ ÒØ ÖÚ Ð ¼ ƺÄØ Ê Ê ×× Ò ØÓ ´Òµ ×´Òµº Ì ×´Òµ × ÐÐ Ø × Ø ÒÚ Ö ÒØ Ö ÔÖ × ÒØ Ø ÓÒ Ó ´Òµ « ×´Òµ ¨³ ´Òµ ³¾Ê Ê ÙÒ Ø ÓÒ ´ µ Ç × ÖÚ Ø ÓÒ ½ Ì Ö Ö × Ø ÒÚ Ö ÒØ Ö ÔÖ × ÒØ Ø ÓÒ× Ó ´Òµ Û Ö ÒÓØ Ö ÔÖ × ÒØ Ø Ú ÙÒ Ø ÓÒ× Ó Ë ´Òµ º ÙØÓ ÓÖÖ Ð Ø ÓÒ Ó ÙÒ Ø ÓÒ ´Òµ × Ò Ü ÑÔÐ Ó × Ø ÒÚ Ö ÒØ Ö ÔÖ × Ò¹ Ø Ø ÓÒ Û × ÒÓØ Ö ÔÖ × ÒØ Ø Ú ÙÒ Ø ÓÒ Ó × Ø ÕÙ Ú Ð Ò Ð ×× Ë ´Òµ º Ý × ØÒ Ø ÙØÓ ÓÖÖ Ð Ø ÓÒ ÙÒ Ø ÓÒ Û Ø × Ø ÙØÓ ÓÖÖ Ð Ø ÓÒ ÙÒ ¹ Ø ÓÒ ÙØ ÖØ ÒÐÝ ÒÓØ Ø ÓÖ Ò Ð ´Òµº ÅÓÖ ÓÚ Ö¸ ÐÐ « Ö ÒØ ÙÒ Ø ÓÒ× Û Ú Ø ×ÓÐÙØ Ú ÐÙ Ó Ø Ö ÓÙÖ Ö ØÖ Ò× ÓÖÑ ÕÙ Ð ØÓ Ø ×ÓÐÙØ Ú ÐÙ Ó Ø ÓÙÖ Ö ØÖ Ò× ÓÖÑ Ó ´Òµ Ú Ø × Ñ ÙØÓ ÓÖÖ Ð Ø ÓÒ Ö ÔÖ × ÒØ Ø ÓÒ׺ ËÓ¸ Ø ÙØÓ ÓÖÖ Ð Ø ÓÒ Ö ÔÖ × ÒØ Ø ÓÒ × ÕÙ Ø Ñ ÙÓÙ׺ Image-Based Self-Localization 29 ´ ÖÓ Ô × Ö ÔÖ × ÒØ Ø ÓÒ ´ Èʵµº Ä Ø ÙÒ Ø ÓÒ ´Òµ Æ º ÙÒ Ø ÓÒ £ ½ ´Òµ ´Òµ ´½µ ´ µ ½ × Û Ö ´ µ ´Òµ × Ø × Ö Ø ÓÙÖ Ö ÌÖ Ò× ÓÖÑ Ó ´Òµ Ò Ø× ÒÚ Ö× ¸ × ÐÐ Ø Þ ÖÓ Ô × Ö ÔÖ × ÒØ Ø ÓÒ Ó ´Òµº Ä ÑÑ ½º Ä Ø ÙÒ Ø ÓÒ× ´Òµ¸ ´Òµ Ê Ô Ö Ó ÓÒ ÒØ ÖÚ Ð ¼ Æ Û Ø ÒÓÒ¹Þ ÖÓ ¬Ö×Ø ÖÑÓÒ ¸ º º ´½µ ¼¸ ´½µ ¼¸ Ò Ð Ø £ ´Òµ¸ £ ´Òµ Ê ¬Ò Ø ÓÒ Ô ÖÓ ¬Ò Ë ÓÒ ÒØ ÖÚ Ð ¼ Ý ´ µº Ì Ò¸ ³¾Ê ´Òµ ÈÖÓÓ º Ä ÑÑ × Ú Ö ¬ ½¼ ÓÖ Ø Ð׺ Ç × ÖÚ Ø ÓÒ ¾ £´Òµ ¨³ ´Òµ Ý ×ØÖ ¬Ò ´µ Ø ÓÖÛ Ö Ý´ µ × ÔÔÐ £ ´Òµ Ë ÖÑÓÒ ´ µ Ø ÓÒ Ó ´¾µ Ò ´ µ ÓÒ ´ µº Ö ÔÖ × ÒØ Ø Ú ÙÒ Ø ÓÒ Ó ´Òµ × ÒÓÒ¹Þ ÖÓ ¬Ö×Ø × Ò ÒÚ Ö ÒØ Ö ÔÖ × ÒØ Ø ÓÒ ´Òµ º Ç × ÖÚ Ø ÓÒ ¿ Á £ ´Òµ Ø Ò £ ´Òµ Ë Éº º º ¬Ò ´Òµ º Ý´ µ Ç × ÖÚ Ø ÓÒ× ¾ Ò ¿ × ÓÛ Ø Ø Ø ÈÊ × ÓÓ Ö ÔÖ × ÒØ Ø ÓÒ Ó Ø Ð ×× Ó Ñ ×Û Ú ÒÓÒ¹Þ ÖÓ ¬Ö×Ø ÖÑÓÒ Ò ÓÐÙÑÒ Ö Ø ÓÒº Ì ÈÊ ××ÙÖ × Ø Ø Ø Ñ × Ø Ò Ø « Ö ÒØ ÔÓ× Ø ÓÒ× Û ÐÐ Ö ÔÖ × ÒØ « Ö ÒØÐÝ Ò Ø Ñ × Ø Ò Ø Ø × Ñ ÔÐ Û ÐÐ Ú Ø × Ñ Ö ÔÖ × ÒØ Ø Ú Ñ º Ç × ÖÚ Ø ÓÒ Ì £ ´½µ ¼º º º ¬Ö×Ø ÖÑÓÒ Ó £ ´Òµ ¬Ò Ý ´ µ ÕÙ Ð× Þ ÖÓ¸ Ç × ÖÚ Ø ÓÒ ´ µ ÜÔÐ Ò× Û Ý Ø Ò Ñ Þ ÖÓ Ô × Ö ÔÖ × ÒØ Ø ÓÒ × Ò Ó× Òº ×ØÖ Ø ÓÖÛ Ö Ò Ö Ð Þ Ø ÓÒ Ó ½ ÈÊ ÓÖ ¾ ÝÐ Ò Ö Ð Ô ÒÓÖ Ñ Ñ × Ò Ú Ý Ö ÔÐ Ò Ø ¾ Ì Ý ¾ Ì ×Ó Ø Ø ´ µ × Ö ÔÐ Ý ½ Á ´Ñ Òµ ´¼ ½µ Ð Á £ ´Ñ Òµ ´ µ Û Ö ´ е Á ´Ñ Òµ × × Ö Ø ÓÙÖ Ö ÌÖ Ò× ÓÖÑ Ó Á ´Ñ Òµº ÜÔ Ö Ñ ÒØ ÙÖ ¾ × ÓÛ× Ö Ð Ñ × Ø Ò Û Ø « Ö ÒØ ÓÖ ÒØ Ø ÓÒ× ´ ¸ µ Ò ÔÓ× Ø ÓÒ ´ ¸ µ Ó Ô ÒÓÖ Ñ Ñ Ö º Á ÐÐݸ Ø Ñ × ´ ¸ µ × ÓÙÐ ÖÐ Ø Ý × Ø ÙØ Ø × × Ú ÓÐ Ø ÝØ ÓÐ Ö Ó Ø ÑÖ Û ×Ø Ý× Ø Ø × Ñ ÔÐ ÒØ Ñ Ù× Ø ÖÓØ Ø × Û Ø Ø Ñ Ö Ò Ý Ó ÐÙ× ÓÒ× Ò Ò × ÒØ × Òº ÙÖ × ¾ ´ µ¸ ´ µ¸ ´ µ × ÓÛ Ø ÈÊ Ó Ø Ñ ×º Ì Ñ × ´ µ Ò ´ µ Ö ÕÙ Ø ÓÖÖ ØÐÝ × Ø ×Ó Ø Ø Ø Ö Ö Ð Ø Ú × Ø × ÐÑÓ×Ø Þ ÖÓ × ÜÔ Ø Ú Ò Ø ÓÙ Ø Ö Û Ö Ò × ÒØ × Ò ºÌ Ö Ð ØÚ × Ø Ó Ø ÈÊ Ó Ø Ñ × ´ µ Ò ´ µ Û Û Ö Ø Ò Ø « Ö ÒØ ÔÓ× Ø ÓÒ× « Ö× ÕÙ Ø ÐÓغ 30 T.

Download PDF sample

Rated 4.66 of 5 – based on 48 votes